Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

Curiosity may have killed the cat, but for data scientists, it can open doors to leadership opportunities.In this episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share how his habit of asking deeper questions about the business transformed him from software engineer #30 at Wayfair to a seasoned technology executive and MIT Sloan MBA candidate.You’ll discover:The critical business questions most technical experts never think to ask [02:21]Why understanding business context makes you better at technical work (not worse) [14:10]How to turn natural curiosity into career opportunities without losing your technical edge [09:19]The simple mindset shift that helps you spot business impact others miss [21:05]Guest BioAndrei Oprisan is a technology leader with over 15 years of experience in software engineering, specializing in product development, machine learning, and scaling high-performance teams. He is the founding Engineering Lead at Agent.ai and is also currently completing an Executive MBA through MIT’s Sloan School of Management.LinksConnect with Andre on LinkedInAndrei’s websiteAgent.ai websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Every data scientist knows the sinking feeling: you’ve done brilliant technical work, but your presentation falls flat with stakeholders.In this Value Boost episode, communications expert Lauren Lang and data analyst Dr Matt Hoffman join Dr Genevieve Hayes to share their go-to pre-presentation checklist to ensure that sinking feeling never happens again.You’ll walk away knowing:The critical business context most data scientists overlook when presenting their work [02:10]How to ensure your technical content works as hard as you do – whether presented live or shared asynchronously [04:42]The “so what” framework that instantly makes your analysis more compelling to leaders [06:57]Guest BioLauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.Dr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.LinksConnect with Lauren on LinkedInConnect with Matt on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their expertise.Their surprising findings about Gen AI’s impact on developer productivity went viral across 75 global media outlets – not because of complex statistics, but because of how they told the story.Here’s what you’ll learn:Why the “last mile” is killing your data science impact – and how to fix it through strategic collaboration [01:00]The counterintuitive findings about Gen AI that sparked global attention (including a 40% increase in code defects) [13:02]How to transform “disappointing” technical results into compelling business narratives that drive real change [17:15]The exact process for structuring your insights to keep executives engaged (and off their phones) [08:31]Guest BioDr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.Lauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.LinksConnect with Matt on LinkedInConnect with Lauren on LinkedInCan Generative AI Improve Developer Productivity? (Report)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.Software developers have known for decades that the real key to productivity lies somewhere else.In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques that transformed his 20-year development career – which you can use to transform your data science productivity right now.Get ready to discover:The Kanban and focus currency techniques that replace notification-driven chaos [02:09]A 90-day planning system that beats imposter syndrome and drives results [03:09]Why two-hour focus blocks outperform constant context switching [04:19]The habit tracking method that helps you consistently “win the day” [06:12]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Why do some data scientists produce results at a rate 10X that of their peers?Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.This episode reveals:Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]How to deliver results faster using modern data architectures and raw data analysis. [13:19]The game-changing mindset shift that transforms data scientists from order-takers into trusted strategic partners. [17:05]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Are your data science projects failing to deliver real business value?What if the problem isn’t the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08]Guest BioBill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies’ AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.John Thompson is EY’s Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ’s 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”.LinksConnect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.But in helping everyone else in the business, data professionals can often forget to help themselves.However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.Guest BioProf. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.Highlights(00:05) Meet Barzan Mozafari(00:50) The role of AI in data engineering(01:36) The birth of Keebo(02:34) Challenges in modern data pipelines(05:41) How Keebo optimizes data warehousing(07:35) AI and ML techniques behind Keebo(08:47) Reinforcement learning in practice(16:23) Guardrails and safeguards in AI systems(26:29) The build vs. buy dilemma(36:03) Future trends in data science and AI(39:36) Final advice for data scientists(40:50) Closing remarks and contact informationLinksKeebo websiteConnect with Barzan on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In the 2002 movie, Minority Report, the future of data interaction is depicted as Tom Cruise standing in front of a computer monitor and literally grabbing data points with his hands. Data interaction is shown to be as easy as interacting with physical objects in the real world.This vision of a world where data is accessible to all was considered to be science fiction when Minority Report was first released. But over 20 years later, we are now at a point where technology has become good enough for this to soon become fact. And its data science that’s making this possible.Or more accurately, it’s the intersection of data science and art.In this episode, Michela Ledwidge joins Dr Genevieve Hayes to discuss how virtual reality and data science can be combined to create interactive data storytelling experiences.Guest BioMichela Ledwidge is the co-founder and CEO of Mod, a studio specialising in real-time and virtual production, and the creator of Grapho, a VR platform that lets non-technical users examine and manipulate graph data. She is also the writer and director of A Clever Label, a world-first interactive documentary.Highlights(00:05) Meet Michela Ledwidge(02:04) Michela’s journey from Commodore 64 to interactive filmmaking(06:40) The birth of Mod and remixable films(14:48) Exploring graph databases and data science techniques(25:33) The future of data science and AI in creative industries(32:27) Grapho: Data science + storytelling in virtual reality(48:29) The future of data science and storytelling(49:37) Conclusion and contact informationLinksGrapho websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”When Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps, can be a real issue when it comes to data-driven decision making.In this episode, Matt O'Mara joins Dr Genevieve Hayes to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to help address this issue.Guest BioMatt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status.Highlights(00:55) Understanding information gaps(02:33) Matt O'Mara's journey and insights(04:58) Real-world examples of information gaps(07:30) The impact of information gaps on society(11:54) Organizational challenges and solutions(25:55) Critical information sources and management(31:33) Developing an information lens(42:47) The role of data scientists in addressing information gaps(45:29) Conclusion and contact informationLinksi3 websiteConnect with Matt on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The idea of targeted marketing is nothing new. Even before the advent of computers and data science, businesses have always tried to optimise their advertising campaigns by tailoring their advertisements to their ideal buyers.Data science allowed businesses to become more effective at this targeting. However, it was still necessary for businesses to manually create the advertising content they wanted to share with their target buyers. That is, until recently.In this episode, Hikari Senju joins Dr Genevieve Hayes to discuss how advances in AI technology have made it possible to generate personalised advertising content, optimised to produce the best results, and what that means for content creators.Guest BioHikari Senju is the founder and CEO of Omneky, an AI platform that generates, analyzes and optimizes personalised advertising content at scale. He is a Harvard computer science graduate and also co-founded tutoring app Quickhelp, which he later sold to Yup.com.Highlights(02:06) How OmneKey works(03:29) Personalisation in advertising(06:35) The role of human input in AI-generated content(10:45) Impact of AI on the advertising industry(15:09) Hikari Senju’s journey and insights(19:53) Technical deep dive into OmneKey(25:54) The competitive landscape of AI(32:10) The future of content and AI(40:26) Conclusion and final thoughtsLinksOmnekey websiteConnect with Hikari on LinkedInFollow Hikari on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.However, it’s not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it’s the lack of a well-established business practice that is almost always to blame.”In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.”Guest BioDr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast.Highlights(01:21) Challenges in machine learning deployment(05:00) The importance of business involvement in ML projects(15:39) Defining bizML and its steps(25:32) Understanding predictive analytics(26:52) Challenges in model deployment and MLOps(29:12) BizML for generative and causal AI(31:25) Exploring uplift modeling(35:45) Gooder AI: bridging the gap between data science and business value(45:45) Beta testing and future plans for Gooder AI(47:35) Final advice for data scientistsbLinksBizML websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?That’s where causal analytics can help. However, causal inference is rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.In this episode, Joanne Rodrigues joins Dr Genevieve Hayes to discuss how techniques drawn from the social sciences, in particular, causal inference, can be combined with data science techniques to give data scientists the ability to understand and change consumer behaviour at scale.Guest BioJoanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science and demography. She is the author of Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights and the founder of health technology company ClinicPriceCheck.com.Highlights(00:49) Combining social sciences with data science(02:01) Joanne’s journey from social sciences to data science(04:15) Understanding causal inference(07:40) Real-world applications of causal inference(12:22) Challenges in causal inference(19:41) Correlation vs. causation in data science(26:12) Operationalising randomness in experiments(27:16) Observational experiments vs. medical trials(27:47) Designing experiments with existing data(28:50) Challenges in natural experiments(29:55) Ethical considerations in experimentation(31:50) Qualitative frameworks in causal inference(35:58) Integrating causal inference with machine learning(38:59) Common techniques in causal inference(41:02) Marketing causal inference to management(43:48) Ethical implications of predictive modelling(48:08) Final advice for data scientistsLinksConnect with Joanne on LinkedInJoanne’s websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

With all the reports about the spread of misinformation and disinformation on social media, sometimes it feels like one of the biggest threats to democracy is technology. But no technology is inherently good or bad. It’s how you use it that matters. And just as technology has the potential to harm democracy, it also has the potential to enhance it.In this episode, Vikram Oberoi joins Dr Genevieve Hayes to discuss how he has been using generative AI and large language models (LLMs) to enhance people’s access to NYC council meetings through his work on citymeetings.nyc.Guest BioVikram Oberoi is a software engineer, fractional CTO and co-owner of Baxter HQ, a boutique early-stage tech product development firm. He also built and operates citymeetings.nyc, an LLM powered tool to make New York City council meetings accessible.Highlights(00:00) Meet Vikram Oberoi(01:31) Overview of citymeetings.nyc(07:50) Vikram’s journey into local politics(12:05) Technical aspects of citymeetings.nyc(18:41) Dealing with AI hallucinations(25:00) Understanding the different types of AI errors(26:05) Case study: Honeycomb’s query feature(26:59) Reinforcement learning with human feedback(28:32) Choosing between Claude and GPT(31:42) The importance of context windows(40:31) Effective prompt engineering tips(46:11) Final advice for data scientistsLinkscitymeetings.nycVikram’s websiteVikram’s talk at NYC School of Data about citymeetings.nycFollow Vikram on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.Guest BioAndrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.Highlights(00:06) Meet Andrew Einhorn(02:54) Andrew’s journey from public health to data science(07:55) The birth of Levelfields(19:35) Event-driven investment insights explained(26:22) AI and data science behind Levelfields(36:36) User experience and customisation(41:03) Future developments and final adviceLinksLevelfields websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.Guest BioBrian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.HighlightsIntroducing Brian T. O’Neill (00:19)Brian’s journey from music to data product design (02:16)Understanding the real needs of stakeholders (06:45)The importance of user-centered design in data products (09:33)Gaining insights through direct user interaction (12:16)Focusing on business and user experience outcomes (17:48)Debunking the myths of self-serve analytics and dashboarding (22:46)Data platforms vs. data products (27:26)Defining a data product: the value exchange principle (29:08)Designing human-centered data products (32:56)The CED framework: conclusions, evidence, data (36:01)Final advice for data scientists (45:06)LinksBrian’s mailing listDesigning for AnalyticsData Product Leadership CommunityCED frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.Guest BioDr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society’s Biggest Problems.Highlights(00:00) Meet Dr. Eric Daimler(01:46) Eric’s role in the Obama Administration(06:32) Challenges in government data integration(10:31) The importance of technical expertise in policy(16:06) Founding Connexus AI(18:09) Understanding category theory(20:51) Applications of Conexus AI(27:16) The future of AI: safe and symbolic(38:35) Insights from Eric’s upcoming book(47:49) Advice for data scientists and final thoughtsLinksConnect with Eric on LinkedInConexus AI websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.Yet, in building a data team, many organisations assume the only solution is to build from within. And although this may be the right solution for some organisations, building a solution isn’t right for all.In this episode, Collin Graves joins Dr Genevieve Hayes to discuss what a bought solution might look like in the data science space, and whether it is right for you.Guest BioCollin Graves is the CEO of North Labs, a leading fractional cloud data analytics firm that helps growing companies become data-driven. Before founding North Labs, he served with distinction in NATO Special Operations during his tenure with the US Air Force. He is also the author of the upcoming Data Revolution: Leading with Analytics and Winning from Day One.Highlights(01:43) Collin’s journey from the US Air Force to data science (09:53) The birth of North Labs: a fractional data analytics firm (12:02) Scaling a one-man operation to a thriving business (13:58) The challenges of using data in the industrial and manufacturing sector (28:41) The power of outsourcing data science (34:09) The future of data teams and the role of in-house expertise (41:44) Insights from Collin’s upcoming book (46:17) Final thoughts and advice for data scientistsLinksConnect with Collin on LinkedInNorth Labs websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.Guest BioKirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.Highlights(00:19) Meet Kirk Marple(01:22) Leveraging knowledge graphs and RAG(06:08) Challenges with named entity extraction(09:16) Cost implications of LLMs(12:17) Deep dive into RAG(16:58) Vector search explained(20:49) Graph databases and RAG(38:58) Future of RAG and AI(43:08) Final thoughtsLinksConnect with Kirk on LinkedInGraphlit websiteFollow Graphlit on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but unable to create value from their work. However, this doesn’t necessarily have to be the case.In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists and their managers can use to make data science teams profitable.Guest BioDouglas Squirrel has been coding for forty-five years and has led software teams for twenty-five. He uses the power of conversations to create insane profits in technology organisations of all sizes. His experience includes growing software teams as a CTO in startups; consulting on product improvement; and coaching a wide variety of leaders in improving their conversations, aligning to business goals, and creating productive conflict.HighlightsDouglas Squirrel’s journey: From CTO to profitability guru (00:00)Integrating data science with business goals (10:58)The surprising technological growth in Africa (17:38)Overcoming the Walled Garden: strategies for tech team success (19:14)The Lean Startup approach to data science (26:48)The importance of direct feedback in data science (32:50)Transforming data science with human empathy (33:39)Leveraging action science for effective communication (42:46)Elephant Carpaccio (47:41)Techniques for data scientists to create business value (51:22)Creating productive conflict for business innovation (53:43)Final thoughts and resources (01:00:28)LinksDouglas Squirrel’s websiteSquirrel SquadronConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss the impact of the data scientist on the creation of the next generation of data orchestration tools.Guest BioSandy Ryza is a data scientist turned data engineer who is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. He is also the co-author of Advanced Analytics with Spark.HighlightsWelcome to Value Driven Data Science (00:00)Introducing Sandy Ryza and his journey from data scientist to data engineer (01:30)Navigating the challenges of creating consistent data definitions within teams (05:11)The birth and development of Dagster (11:32)Dagster: A tool designed for data scientists (20:54)Final thoughts and advice for data scientists (37:29)LinksConnect with Sandy on LinkedInFollow Sandy on XDagsterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

From BuzzFeed Quizzes to the national census, it’s impossible to get through life without encountering surveys. However, not all surveys are created equal. As with everything else in data science, garbage going in will inevitably lead to garbage coming out.In this episode, Kyle Block joins Dr Genevieve Hayes to look at practical techniques for designing surveys to ensure they deliver value, as well as approaches to analysing survey results, to maximise that value.Guest BioKyle Block is Head of Research at Gradient, an analytics agency that combines advanced statistical and machine learning techniques to answer difficult marketing challenges. He holds a Masters in Spatial Analysis from the University of Pennsylvania and has spent his career helping managers use data to make important decisions.Talking PointsWhat good survey design looks like.Advice on how to design effective surveys.How list experiments can be used to uncover true opinions around sensitive topics.How data science techniques can be applied to survey data analysis to maximise its value.What the future might hold for survey data analysis.LinksConnect with Kyle on LinkedInGradient WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Most people have come to accept that the price of living in a technological world, and its associated convenience, is some loss of data privacy. However, few realise just how much privacy they are giving up.In this episode, Dr Katharine Kemp joins Dr Genevieve Hayes to discuss data privacy challenges for consumers and data scientists in the age of AI.Guest BioDr Katharine Kemp is an Associate Professor in UNSW’s Faculty of Law and Justice and Deputy Director of the Allens Hub for Technology, Law and Innovation. Her research focuses on competition, data privacy and consumer protection regulation, including their application to digital platforms.Talking PointsWhat types of data are companies collecting about their customers?How companies currently de-identify customer data to ensure consumer privacy is protected.The effectiveness of data de-identification methods at truly protecting the privacy of individuals.The state of current consumer data privacy laws and how they are likely to evolve.The impact of generative AI tools, such as ChatGPT, on consumer data privacy.LinksUNSW Allens Hub for Technology, Law and InnovationKatharine’s Research (SSRN Page)Consumer Policy Research CentreSingled Out ReportConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-making process can become more complicated, in the form of sequential decision problems.Guest BioWarren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.Talking PointsWhat is a sequential decision problem?Real-life examples of sequential decision problems and the disciplines in which they occur.The four main classes of techniques for solving sequential decision problems.How Warren’s approach to addressing sequential decision problems differs from the standard approach in this space.The challenges of implementing sequential decision analysis techniques in practice.LinksConnect with Warren on LinkedInWarren’s website (SDA Links)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

According to the Interview Valet 2023 State of Podcast Guesting Annual Report, there are over 380,000 active podcasts in the world right now, with the average podcast episode receiving just 150 downloads within 30 days of its release.So, for individuals and organisations looking to use podcast marketing to grow their business, just booking podcast guest appearances isn’t enough. It’s necessary to use a targeted strategy based on data.In this episode, Tom Schwab joins Dr Genevieve Hayes to discuss how Interview Valet uses data to optimise business results in podcast interview marketing.Guest BioTom Schwab is the founder and Chief Evangelist Officer of Interview Valet and the author of Podcast Guest Profits and One Conversation Away. He is also an engineer whose first job out of college involved running nuclear power plants in the US Navy.Talking PointsWhat is podcast interview marketing and how it differs from traditional digital marketing approaches?How Tom uses data to inform podcast guest marketing strategies at Interview Valet.The most important metrics for targeting podcast marketing and optimising return on investment.What makes a top podcast?How Tom’s use of data and analytics has evolved over time.LinksInterview ValetConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Start-ups and data science go hand in hand, but usually when people think about how data science can help start-ups, it’s with regard to product development and enhancement. However, it doesn’t matter how great a start-up’s product is, if the financials are a mess, the business is going to struggle.This is where data science can also help start-ups, in the form of financial modelling and analysis.In this episode, Lauren Pearl joins Dr Genevieve Hayes to discuss her work in helping start-up founders translate their business ideas into maths via financial models.Guest BioLauren Pearl is a CEO-turned-CFO who helps start-up founders work better with financial data. She holds an MBA from NYU’s Stern School of Business and is the resident start-up finance expert at NYU’s Berkley Centre for Entrepreneurship.Talking PointsWhat is meant by financial modelling?The challenges of building financial models with little or no data.Why is it important for founders to understand their financials.The potential consequences of not understanding financial data.How founders can use data and technology more generally to help in running their business.LinksConnect with Lauren on LinkedInLauren Pearl ConsultingConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data science is among the most in-demand skills of the 21st century, with opportunities existing for data scientists to make a difference and earn good money as an employee in a range of industries. Yet there has also never been a better time to be a data science entrepreneur (or datapreneur).But for data scientists who have never experienced the entrepreneurial life and who are used to the security of a steady pay check, making the transition from employee to entrepreneur may seem like an impossible leap, regardless of how desirable it may seem.In this episode, David Shriner-Cahn joins Dr Genevieve Hayes to discuss how data scientists can escape the corporate world and make the transition from employee to datapreneur.Guest BioDavid Shriner-Cahn is the podcast host and community builder behind Smashing the Plateau, an online platform offering resources, accountability, and camaraderie to high-performing professionals who are making the leap from the corporate career track to entrepreneurial business ownership.Talking PointsHow entrepreneurship differs from being a regular employee, particularly with regard to mindset.The advantages and disadvantages of each way of making a living.Making the transition from employment to entrepreneurship and how to gauge if entrepreneurship is right for you.Building your network as an entrepreneur.How taking a sabbatical can help ease the transition between being an employee and an entrepreneur.The value of community.LinksSmashing the PlateauConnect with Genevieve on LinkedInValue Driven Data Science has recently featured in Feedspot’s list of the 4 Best Australian Data Science Podcasts. Be among the first to hear about the release of each new podcast episode by signing up HERE

Depending on who you speak to blockchain and cryptocurrency are either the way of the future or the scam of the century. But few would be able to tell you what either of them actually is – including among data scientists for whom data and technology are a way of life. In this episode, Luke Willis joins Dr Genevieve Hayes to demystify blockchains, cryptocurrency and the data behind them.Guest BioLuke Willis is the dApp UX guy. He’s a web3 developer with extensive front end and UX experience. He’s also the founder of the Koin Press where he writes a regular newsletter, hosts the Koin Press podcast and helps others make their dApp ideas a reality.Talking PointsWhat is the blockchain?The different types of blockchains and the differences between them?How the blockchain relates to cryptocurrency.What is a dApp and Luke’s experiences in building them.How data is stored on the blockchain and how it can be accessed.LinksLuke’s WebsiteKoinos BlocksKoinerEtherscanConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When data science first became the must-have skill of the 21st century, organisations were fighting to recruit the best and brightest data science talent. But the glory of having a data scientist on staff was often short-lived, as many organisations soon found they didn’t know what to do with them.Business leaders had been sold the dream of being able to turn their data into business gold but were unable to maximise the value of the data science expertise they had brought in because they couldn’t communicate effectively with their new data science teams.In this episode, Dr Howard Friedman joins Dr Genevieve Hayes to discuss how adopting a customer mindset can help business leaders capitalise on the hidden value of data.Guest BioDr Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modelling teams in the private sector, public sector and academia. He is an adjunct professor, teaching data science, statistics, and program evaluation, at Columbia University, and has authored/co-authored over 100 scientific articles and book chapters in areas of applied statistics, health economics and politics. His previous books include Ultimate Price and Measure of a Nation, which Jared Diamond called the best book of 2012.Talking PointsHow Howard’s personal experiences informed the writing of Winning with Data Science.What business leaders should know, in order to be effective customers of data science teams.How important is it for business leaders to be up to date with the latest data science trends and buzzwords?What data scientists should know in order to work more effectively with business leaders.Howard’s previous book, Ultimate Price.How data scientists and economists go about placing a price on human life.LinksConnect with Howard on LinkedInHoward’s websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Correlation does not equal causation, as anyone who has studied statistics or data science would know. But understanding causality isn’t just important when you’re developing models.If you’re working in business and want to be recognised for your work, it’s essential to be able to demonstrate causality between what you do and the benefit flowing through to the business.In this episode, Mark Stouse joins Dr Genevieve Hayes to discuss how data science can be used to comprehend the underlying cause-and-effect relationships in business data.Guest BioMark Stouse is the CEO of Proof Analytics, an AI-driven marketing analytics platform. Prior to becoming an analytics software CEO, Mark had a successful career in B2B marketing and in 2014 was named Innovator of the Year at the Holmes Report In2 SABRE Awards for his work in tying marketing and communication investment to key business performance metrics.Talking PointsThe benefits to organisations of understanding causality.How such techniques can be applied to use cases and disciplines beyond marketing analytics.How data scientists can drive conversations about analytics at the C-suite level to maximise their impact.The potential future impact of generative AI on data science and the world in general.LinksConnect with Mark on LinkedInProof AnalyticsConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The insurance sector owes its existence to data and insurers were some of the first companies to utilise data expertise. Yet, being an early adopter isn’t always as great as it seems. And many big insurers are now discovering the challenges of bringing their long-established data systems into the 21st century.In this episode, Maria Ferrés joins Dr Genevieve Hayes to discuss the complexities of creating order from data chaos in the insurance industry.Guest BioMaria Ferrés is an actuary with extensive experience throughout Europe and Australia, who now specialises in establishing the enterprise data functions of multinational insurers. She is currently the Enterprise Data Officer at trade credit insurer Atradius and she also advises companies within the insurtech space on the use of data to comply with Data Protection laws.Talking PointsThe ideal state of an insurer’s enterprise data capabilities.How to transform insurers’ data capabilities from their present, often chaotic state, to this ideal.The challenges in transforming insurers’ data capabilities.Where data scientists fit into the transformation process.How to overcome resistance encountered while transforming the data capabilities of an organisation.The impact of the GDPR on enterprise data capabilities and on the work of people using insurance data, including data scientists and Insurtechs.LinksConnect with Maria on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

ChatGPT was one of the best things to ever happen to data science – not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream. However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that. Guest BioDr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.Talking PointsThe data science behind LLMs.How TeamSolve’s Lily, compares to ChatGPT and the advantages of a domain-specific, private chatbot, such as Lily, over a more general, public chatbot, such as ChatGPT.How knowledge graphs can be combined with LLMs to overcome many of the shortcomings of LLMs.The changing attitudes of organisations around the use of generative AI tools.What the emergence of cutting-edge AI tools, such as LLMs, mean for more traditional data science tools, such as analytics dashboards.The future of generative AI, and the potential benefits and risks to society.LinksTeamSolveConnect with Mudasser on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Despite its conservative reputation, the financial services industry has always been a big adopter of cutting-edge technologies. Dating back more than a century, it’s also been one of the biggest employers of people with technology and data-related skills. But what does the future hold for the use of tech in the financial services industry?In this episode, Ben Shapira joins Dr Genevieve Hayes to discuss what this future might look like and how technology is being used right now to improve the lives of consumers.Guest BioBen Shapira is a digital strategist and UX specialist turned tech entrepreneur. He is the founder and Chief Product Officer of Australian fintech start-up Dinero, as well as being a lecturer in the Master of Media and Communication program at Swinburne University.Talking PointsWhere the financial services industry is heading, regarding the use of technology and how this will affect the lives of consumers.The types of data modelling and analysis that are possible because of the data produced by these new technologies.What is Dineiro and how data informed its creation.The impact of data security considerations on financial services organisations’ ability to adopt new technologies and make use of the data they produce.Advice for data scientists looking to build a career in marketing and advertising.How marketing techniques can be applied to data science to make data scientists more effective, regardless of their industry.LinksConnect with Ben on LinkedInDineiroConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data science is only useful if it can create value. And one way that value can be created is by using data to influence decision-making. Yet, to influence decisions, data scientists need to effectively communicate the outcomes of their work – which is something many struggle with. This is because effective data science communication is about more than just rattling off statistics and expecting your end users to piece them together.In this episode, Dr Selena Fisk joins Dr Genevieve Hayes to discuss how data scientists can improve their communication by using those numbers to tell a story.Guest BioDr Selena Fisk is a data storyteller and researcher, with a background in education, who now works with the corporate sector to develop data-informed strategies. She is also the author of a number of books, including I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World and Data-Informed Learners: Engaging Students in their Data Story.Talking PointsWhat is data storytelling and how does it differ from data visualisation?How can data scientists make use of storytelling techniques to maximise the impact of their work?The difference between being data-informed and data-driven, and what that means for schools and businesses.How data is being used in schools to inform learning and improve educational outcomes.How educators can involve students in the data conversation, and what data scientists can learn from this when it comes to engaging business stakeholders in their work.LinksSelena’s WebsiteConnect with Selena on LinkedInFollow Selena on TwitterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.Guest BioTodd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.Talking PointsWhat are some of the limitations of traditional financial modelling?Examples of what can happen when traditional financial modelling goes very wrong.How to do financial modelling the right way.The Engineer’s Fallacy or why you shouldn’t apply pure data science to financial planning.The implications of this for fields outside of the financial services industry.LinksTodd’s WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

If you look at the list of the greatest inventions of the 20th century, you’ll find they all have two things in common. From tea bags to toasters and from cell phones to cellophane, they all take the form of physical objects, and all are, or at least were, protected by patents.Yet, since the turn of the century, the nature of inventions has changed significantly. And many of the greatest inventions of this century now take the form of computer code or models.But how do you protect an invention you can’t physically touch?In this episode, Helen McFadzean joins Dr Genevieve Hayes to discuss the intersection of artificial intelligence and intellectual property.Guest BioHelen McFadzean is a patent and trademark attorney, with a background in artificial intelligence and mechatronics engineering. She has successfully obtained patents, trademarks and designs for businesses in Australia and overseas in a large number of technology areas including machine learning and image classification, automation, smart devices, audio signal processing, embedded software, and control systems.Talking PointsWhat is the difference between patents, trademarks and copyrights?How do you know if an AI/ML-based invention is worth protecting and how do you protect it if it is?What parts of an AI/ML-based invention can be protected through patent law?The importance of good communication in capturing IP.What happens if an invention was invented by a generative AI, rather than a human?LinksConnect with Helen on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Most Intro to Machine Learning courses cover supervised learning and unsupervised learning. But did you know there is also a third type of machine learning, which was used in the development of ChatGPT and is likely to become increasingly important in the not too distant future?In this episode, Prof Michael Littman joins Dr Genevieve Hayes to discuss reinforcement learning – the other type of machine learning – as well as his new book, Code to Joy: Why Everyone Should Learn a Little Programming.Guest BioProf. Michael Littman is an award-winning Professor of Computer Science at Brown University, specialising in reinforcement learning; is co-creator of the Machine Learning and Reinforcement Learning courses offered as part of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program; and is currently serving as Division Director for Information and Intelligent Systems at the (US) National Science Foundation. He is also the author of Code to Joy: Why Everyone Should Learn a Little Programming.Talking PointsWhat is reinforcement learning and why has it traditionally been seen as “the other type of machine learning”?Current and future applications of reinforcement learning.How reinforcement learning is being used to create business value.Michael’s new book, Code to Joy and why everyone should learn to code.How non-programmers can get started with coding and what it would mean for the world if more people did code.LinksMichael’s WebsiteFollow Michael on TwitterComputing Up PodcastMachine Learning A Cappella (Thriller Parody)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data science sits at the intersection of Computer Science and Statistics, so it comes as no surprise that many of the best data scientists have a computer science or software development background. And those that don’t? Well, there’s a lot they can learn from software developers.In this episode, Ethan Garofolo joins Dr Genevieve Hayes to discuss techniques from software engineering and software development that you can use to become a better data scientist.Guest BioEthan Garofolo is a software developer and software architect, specialising in microservice-based projects and using Lean and DevOps principles to make software development teams more effective. He is the author of Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS and runs the Utah Microservices Meetup group.Talking PointsWhat is the difference between a software engineer, software developer and software architect?The impact of team structure and communications on software design.How Lean and DevOps principles can be used to make technical teams run more effectively.The benefits of pair programming and mob programming.What is test-driven development and how can it be used to enhance the quality of data science outputs?Using ChatGPT/AI to enhance developer capabilities.LinksEthan’s WebsiteConnect with Ethan on LinkedInFollow Ethan on TwitterFollow Ethan on TwitchConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The saying goes that if you’re not paying for the product, then you are the product. And every time you interact with the digital world, there’s a good chance your data is going to be harvested for some alternative use.In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data.Guest BioDr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research.Talking PointsThe rights and responsibilities of consumers and organisations, when it comes to personal data.How organisations currently collect consumer data and what they are using that data for.The use of “harvested” data in AI tools, such as ChatGPT and Stable Diffusion.What data scientists should be aware of when sourcing data for their work.How to source data ethically.LinksConnect with Kate on LinkedInFollow Kate on TwitterCHOICE – Consumers and DataConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

We all want to live long, happy and healthy lives, and in the age of technology, it comes as little surprise that people are turning to data for help.Between smart watches, Oura rings and even just fitness apps like Strava, we’re all generating massive quantities of personal health and fitness data each day, sometimes literally in our sleep. But that data is only valuable if it can be converted into useful insights.In this episode of Value Driven Data Science, Dr Torri Callan joins Dr Genevieve Hayes to discuss how health tech start-ups, such as UAre, are now looking to do just that.This is the third part of a three-part special focussing on the use of data science in start-ups.Guest BioDr Torri Callan is the Data Scientist at Australian health tech start-up UAre, as well as working as a data scientist with fintech start-up Spriggy. He has spent the past 5 years setting up AI and automated risk management for leading finance companies in Australia.Talking PointsHow UAre is using data science to encourage people to exercise more and improve their lives.The challenges of combining data from multiple sources.How to go about building a data product from absolutely nothing.The importance of domain knowledge and research when building a health tech app.What are Bayesian methods and how can they raise the level of rigour of statistical analysis?LinksConnect with Torri on LinkedInUAreConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Once upon a time, data scientists needed to develop programming skills to rival those of software engineers, and this limited the ability of people without such skills to make use of AI. But recently, this has changed, with the huge number of no-code and low-code tools entering the market.In this episode, I’m joined by Geo George to discuss how start-ups are leading the way in leveraging such tools, and in the process, helping to make AI and data science available to all.This is the second part of a three-part special focussing on the use of data science in start-ups.Guest BioGeo George is a director and co-founder of Mayfly Accelerator, a company that helps founders build, grow and scale disruptive start-ups. He is also a start-up founder in his own right and has experience as an executive in the Government sector, with a focus on strategy and risk management.Talking PointsHow are start-ups facilitating the democratisation of AI and data science.The consequences of this democratisation for current and aspiring data scientists.How no code and low code AI and data science tools can be used to develop AI-driven products.The impact of ChatGPT on start-ups, businesses and education in general.LinksConnect with Geo on LinkedInMayfly AcceleratorConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin. In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.This is the first part of a three-part special focussing on the use of data science in start-ups.Guest BioDr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.Talking PointsThe key differences between doing data science/AI in an academic setting and doing it in the commercial world.How to go about translating academic research into commercially viable AI-based products.What makes for a successful university/commercial collaboration?The challenges of building AI products from scratch, including lack of data and how to tell if a project has the potential to be commercially viable.Protecting IP for AI systems.The impact of having real end users on AI product development.The most valuable skills data scientists can develop for building commercial AI technologies.LinksConnect with Jeroen on LinkedInProofTecConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

AI technology has now reached the point where it can potentially damage the reputation of an organisation, if improperly managed. As a result, many data scientists are now becoming very interested in understanding AI ethics and responsible AI.In this episode of Value Driven Data Science, Chris Dolman joins Dr Genevieve Hayes to discuss strategies organisations and data scientists can apply to de-risk automated decisions, and in doing so, avoid an AI scandal.Guest BioChris Dolman is the Executive Manager, Data and Algorithmic Ethics at Insurance Australia Group, a Gradiant Institute Fellow and regularly contributes to external research on responsible AI and AI ethics. In 2022, he was named the Australian Actuaries Institute’s Actuary of the Year, in recognition of his work around data ethics, and was also included in Corinium Global Intelligence – Business of Data’s list of the Top 100 Innovators in Data and Analytics.Talking PointsThe risks associated with the use or design of AI-based decision-making tools.How these risks might potentially be amplified in the case of new, cutting-edge algorithms, such as ChatGPT.Why “boring” is sometimes better, when it comes to AI.Examples of where things have gone wrong in the past.Strategies for identifying and avoiding potential AI scandals before they occur.The regulation and governance of AI, both now and in the future.LinksConnect with Chris on LinkedInDe-Risking Automated Decisions ReportCheckmate HumanityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The launch of Chat-GPT turned the business world upside down and left many people wondering about the future of their careers. How do you compete against AI? One solution is by delivering a superior customer experience.In this episode, Dasun Premadasa joins Dr Genevieve Hayes to discuss why technical people often trip up when it comes to customer experience and what data scientists can do to overcome these issues.Guest BioDasun Premadasa is the founder of DASCX, an independent business analyst consultancy that helps businesses with their digital transformations and IT project delivery. He is also the host of the DASCX Show on YouTube.Talking PointsHow delivering a superior customer experience can boost your value as a data scientist.Why technical people, such as data scientists, tend to neglect CX.What does good CX look like?The consequences of bad CX for data scientists and end users.The importance of identifying the right customer when pitching a data science solution.Strategies data scientists can employ to improve the experience of their end user.LinksConnect with Dasun on LinkedInDASCX Show episode with Dasun and GenevieveDASCXConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

From social media to electricity grids and the internet itself, we live in a highly interconnected world. But traditional data science techniques don’t adequately allow for the relationships that can exist between data points in such networks. This is where graph data analysis comes into play. In this episode, Dr Alessandro Negro joins Dr Genevieve Hayes to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.Guest BioDr Alessandro Negro is the Chief Scientist at GraphAware, the world’s #1 Neo4j consultancy, and Managing Director at GraphAware Italy. He is also the author of Graph-Powered Machine Learning and the recently released Knowledge Graphs Applied.Talking PointsWhat is graph data and how does it differ from structured data?Use cases for graphs and graph databases?What is a knowledge graph, how are they created and what are their benefits?How can graphs be used to power machine learning?How can machine learning algorithms be used to build knowledge graphs?Steps data scientists can take to get started with graph data science and knowledge graphs.LinksConnect with Alessandro on LinkedInGraph-Powered Machine LearningKnowledge Graphs AppliedConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data science is an in-demand skill. Yet, many data scientists find it challenging to get started in the industry and to differentiate themselves from other data scientists once they find a job.In this episode, Jonathan Stark joins Dr Genevieve Hayes to discuss how data scientists can find their niche and build a reputation as a data science authority.Guest BioJonathan Stark is a former software developer who now helps independent professionals make a living while increasing their impact on the world. He is the author of Hourly Billing Is Nuts, the host of the podcast Ditching Hourly and the co-host of The Business of Authority.Talking PointsHow data scientists can build their authority and move away from being viewed as commodities.The benefits of specialisation, both as an independent professional, and as an employee of a larger organisation.Is it necessary to manage staff in order to establish your credibility as an authority in data science?How your authority as a data scientist, once established, could potentially be leveraged both within the corporate world and as a springboard into an independent career.LinksJonathan’s WebsiteDitching HourlyThe Business of AuthorityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

“Data science unicorns” are those rare people who “understand the (data) problem they seek to resolve, have the mathematical expertise to analyse the problem and possess the computing skills to covert this knowledge into outcomes.” In fact, they are considered so rare that some people have suggested they don’t really exist. Yet, although nobody is born a data science unicorn, organisations with the right know-how can create them.In this episode, Dr Peter Prevos joins Dr Genevieve Hayes to discuss his work in creating data science unicorns from water industry subject matter experts around the world.Guest BioDr Peter Prevos is a civil engineer, social scientist (and amateur magician) who manages the data science function at Coliban Water in regional Australia and runs courses in data science for water professionals. He is also the author of a number of books including Principles of Strategic Data Science and the recently released Data Science for Water Utilities.Talking PointsWhy Linux is the best operating system for data science.How the social sciences can make you a better data scientist.Creating data science unicorns in the water industry.The similarities and differences between data science in the water industry and in other industries.What data scientists can learn from the world of theatrical magic.LinksConnect with Peter on LinkedIn Peter’s websiteComputer Magic: Software Illusions and DeceptionsComputer Science 4 FunConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Are you familiar with “environmental justice”? It’s all about equitable access to environmental amenities and the equitable distribution of pollution, and has its roots in the American Civil Rights movement of the 1960’s and 1970’s.In this episode, Robin Rotman and Amber Spriggs join Dr Genevieve Hayes to discuss the environmental justice movement and how open access GIS-based tools are being used to achieve environmental justice in the USA today.Guest BioRobin Rotman is an Assistant Professor of Energy and Environmental Law and Policy at the University of Missouri-Columbia. She is also a qualified lawyer, focussing on energy, environmental, and natural resource issues, and is a Counsel at Van Ness Feldman, a law firm in Washington DC.Amber Spriggs is a civil engineering Masters student at the University of Missouri-Columbia with a research focus on hydrology, hydraulic engineering, GIS-based risk assessment, and flood insurance policy.Talking PointsWhat is environmental justice?Why the environmental justice movement and the American Civil Rights movement are one and the same.The role of data and analytics in achieving environmental justice both now and when the term was first coined.Examples of how spatial data analysis has been used to achieve environmental justice.How similar techniques could potentially be used to achieve positive outcomes for the community in other ways.The role of data and analytics in legal proceedings relating to environmental justice.LinksConnect with Robin on LinkedInConnect with Amber on LinkedInEJScreenConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data scientists are constantly being told of the importance of effective communication for their career success. But this advice typically translates to being able to communicate effectively the results of their work. One aspect of communication that is often overlooked is conversational communication.In this episode, Julia Lessing joins Dr Genevieve Hayes to discuss the skills and techniques data scientists can combine to make their workplace conversations a lot easier.Guest BioJulia Lessing is the principal actuary and Director of Guardian Actuarial, which specialises in helping clients use data to solve complex people-oriented problems, and runs the Guardian Actuarial Leadership Program and the Easier Conversations course. She is also the host of the We Are Actuaries podcast and has trained and served as a Lifeline phone counsellor.Talking PointsThe importance of effective conversations in the workplace.Common stumbling blocks for technical people, when it comes to effective communication.The potential consequences of poor workplace conversations and the benefits of good conversations.The key skills involved in conducting an effective conversation.How you go about preparing for important conversationsConducting effective conversations within large groups and how to make meeting communications more effective.LinksConnect with Julia on LinkedInEasier ConversationsConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In December 2022, OpenAI released ChatGPT for public testing and within a week of its launch, the user count exceeded 1 million. For many, ChatGPT provided a first glimpse at what an AI-powered future might look like.In this episode, Dr Genevieve Hayes is joined once again by Dr David Joyner to discuss the implications of AI-driven technology, such as ChatGPT, for education, business and the world in general, and to finish their discussion of Georgia Tech’s OMSCS program.This is the second part of a two-part conversation, which began in Episode 9.Guest BioDr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.Talking PointsThe evolution of human computer interaction.The role of Masters programs vs shorter courses in helping data scientists keep up with the latest technological developments.Why ChatGPT is a game changer for education and business in general.The opportunities and challenges presented by AI chatbots, such as ChatGPT.The importance of understanding the context when analysing data.LinksDavid’s websiteGeorgia Tech’s OMSCSConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

What if you could get a Masters degree in Machine Learning for under US$8000, from a top US university, without quitting your day job or moving location? Georgia Tech’s pioneering Online Master of Science in Computer Science (OMSCS) program offers just that. In this episode, Dr David Joyner joins Dr Genevieve Hayes to discuss OMSCS, the world’s first MOOC-based degree.This is the first part of a two-part conversation, which is continued in Episode 10.Guest BioDr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.Talking PointsWhat is the OMSCS?How OMSCS compares to other Computer Science Masters programs and MOOCs?How online education can help data scientists keep pace with the rapidly changing technological landscape.Challenges and opportunities associated with teaching and learning in the online space.Why Georgia Tech students talk about “getting out” instead of “graduating”.LinksDavid’s websiteGeorgia Tech’s OMSCSConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE