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Explore every episode of the podcast Data Science Leaders

Dive into the complete episode list for Data Science Leaders. Each episode is cataloged with detailed descriptions, making it easy to find and explore specific topics. Keep track of all episodes from your favorite podcast and never miss a moment of insightful content.

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TitlePub. DateDuration
The Rise of the Self-Driving Organization16 Apr 202500:35:26

As organizations race to become AI-driven, data has emerged not just as a resource, but as a strategic advantage. In this episode, Kjell Carlsson sits down with Doug Laney, author of Infonomics and Data Juice, to explore how generative AI is accelerating the journey toward data monetization and organizational autonomy. Doug shares why traditional approaches to data management no longer suffice, how digital twins and agentic AI are redefining business models, and why the future may belong to self-driving organizations.

Tune in to hear:

  • How AI is reshaping the economics of data and enabling new business models
  • Why organizations should embrace agentic AI and digital twins to stay competitive
  • What leaders must do today to build autonomous, AI-native enterprises

The Cultural Shifts That Power AI Adoption02 Apr 202500:47:24

AI strategy isn't just about technology. It's about transformation.

In this episode, CDAO expert, Dr. Shahram Ebadollahi, joins to discuss what it takes to lead AI in an enterprise and why that leadership must go far beyond just selecting the right tools or models. Drawing on his experience building AI teams, Shahram explains why the Chief AI Officer role is one of the most complex, and most critical, positions in modern organizations. Here we talk about the cultural, organizational, and strategic shifts needed to embed AI deeply and responsibly, and how to move from experimentation to value delivery at scale.

Whether you're leading AI or supporting it, this conversation offers a clear-eyed view of what success really requires.

Tune in to hear:

  • Why the Chief AI Officer must balance tech depth with business vision
  • How to structure teams and incentives for lasting AI success
  • How to move from AI experimentation to real business impact

AI Transformation in Government: Lessons from Unit X27 Nov 202400:36:45
Think you have a hard time driving AI adoption in your organization? Try driving AI transformation in the largest, most regulated organization on the planet – the Pentagon. 

In this episode, we sit down with Dr. Chris Kirchhoff – co-author of Unit X: How the Pentagon and Silicon Valley are Transforming the Future of War and former Director of Strategic Planning for the National Security Council – to uncover how the Department of Defense is accelerating the adoption of AI. Dr. Kirchhoff shares invaluable lessons on managing disruption and fostering innovation at scale.
Join us as we discuss:
  • How leadership and experimentation drove the success of the Defense Innovation Unit
  • The importance of developing new frameworks for procurement for AI adoption
  • Insights from modern warfare: drone technology and the shift to consumer-driven innovation
  • Best practices for scaling technological transformation across any large organization

The EU AI Act: Key Strategies for Regulatory Compliance13 Nov 202400:46:32
The EU AI Act, is it the first step towards comprehensive AI regulation that will make us all safer, or is it the scariest thing in AI today, or both?

In this episode we speak with Adam Gale, Field CTO for AI and Regulatory Compliance at NetApp, to demystify the EU AI act and discuss future-proof strategies to ensure compliance.Join us as we discuss:
  • The core requirements of the act and how they impact your use cases
  • Gray areas to look out for in the act
  • Compliance with existing regulations such as DORA and GDPR
  • Core governance capabilities that enable compliance for the EU AI act and future regulation

AI Governance in Action: Lessons from the Trenches25 Oct 202400:38:46
AI governance is no longer a hypothetical consideration—it's critical not only to meet regulatory requirements but also to build trust, drive adoption, and deliver tangible impact with AI. In this episode, we bring together experts Jared Vaudrey and Dr. Dylan Bobby Storey, who have spent years developing AI solutions in heavily regulated industries. They share their hard-won insights on the realities of implementing AI governance across a wide range of organizations and use cases.

Join us as we discuss:
  • The value of AI governance for driving adoption and innovation
  • How governance principles apply to traditional ML and genAI
  • The common challenges of governance 
  • Real-world examples and best practices from advanced teams integrating AI governance
  • How automation helps scale governance and provide the flexibility to support future regulation

Demystifying the Top 5 Questions of AI Governance03 Oct 202400:21:20
AI governance is more important than ever, but confusion reigns about basic questions such as: what it is, why it is important and what we should do about it. As an AI, data science, or business leader, you need to dispel these governance misconceptions in order to manage AI risk and ensure the safety and reliability necessary to drive adoption and impact. The ability of your organization to drive impact with AI, and potentially your career, depends on it.

Join us as host Kjell Carlsson, gives an opinionated take on the basic questions of AI governance:
  • Why you should care about AI governance (and why it goes beyond ethics and regulation)
  • What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)
  • Why AI governance is difficult (and the gap between frameworks and practices)
  • What AI governance looks like in practice (across the AI/ML lifecycle)
  • What we need for AI governance (and the capabilities to do it at scale) 

  
Operationalizing privacy in the age of AI18 Sep 202400:42:49
Data privacy - why is it so important in the age of AI, why is it so difficult, and what should we be doing to improve it in our organizations?

In this episode we speak with Dr. Rebecca Balebako, privacy engineer and Chief Privacy Officer at Lotic.ai, about why AI makes privacy more important than ever, the common misconceptions around data protection, who should own privacy, and the benefits and limitations of privacy enhancing technologies (PETs).

Join us as we discuss:
  • The value of privacy (~5% of annual profit)
  • The many definitions of privacy.
  • The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.
  • Who should own privacy in your organization.
  • PETs vs. data hygiene


For more on Rebecca’s consulting on privacy strategy, coaching and how to build a privacy advocate team see https://www.privacyengineer.ch/ and for more on privacy methods check out the book “Practical Data Privacy” by Katharine Jarmul.
Optimizing Your Architecture for AI Innovation: BARC Survey Results04 Sep 202400:47:03
What capabilities do you need to take advantage of AI and what changes will you need to make to your IT architecture? Well, let’s look at the data.

In this episode, Shawn Rogers, CEO and Fellow at BARC US, shares the results of their survey on how enterprises are optimizing their architecture for AI innovation. Shawn unpacks data on everything from the biggest obstacles to delivering AI impact to how companies are sourcing their AI capabilities. 

Join us as we discuss:
  • The AI talent gap and the strategies that firms are using to close it
  • The myriad AI capabilities that high-readiness firms are implementing
  • The need to manage AI costs upfront to ensure deployment
  • The perpetual question of build vs. buy (or both!)
  • The crucial need for AI governance to ensure approval and adoption


Download the full report here.
Driving Digital Strategy with AI at OneAmerica21 Aug 202400:45:59
How do you drive digital strategy and transformation with AI? Do you need an AI strategy or a business strategy that intelligently leverages AI?

In this episode, we delve into the challenge of driving transformation with AI in insurance with Fu'ad Butt, VP Head of Digital Strategy and Automation at OneAmerica. Fu’ad shares his best practices for identifying and executing AI projects. These range from how to identify the most promising use cases (hint: focus on augmented intelligence, but tie it to business value) to executing them successfully (build a test and learn process, and use multifunctional pods).  

Join us as we discuss:
  • The role of AI in digital strategy today.
  • Overcoming the challenge of aligning AI to business value.
  • Experimenting efficiently with digital twins and a contrarian in the loop.
  • Breaking hierarchies with multifunctional pods for faster impact.

AI-driven Marketing, Optimization, Consciousness and CAIOs07 Aug 202400:47:43
AI is disrupting marketing, but the biggest threat isn’t AI systems misbehaving, it is the unintended consequences of AI systems performing exactly what they were intended to do.

In this interview with Dr. Daniel Hulme, Chief AI Officer at WPP and CEO of Satalia, we discuss the ways that AI is transforming marketing – from accelerating content creation and maximizing activation to exploring the creative landscape and creating “brains” that ensure it is responsible and legal. Also, tune in for fascinating discussions of AI consciousness and what it means to be a Chief AI Officer.    

Join us as we discuss:
  • The greatest GenAI opportunities in marketing and beyond
  • How to maximize AI impact with decision optimization
  • Responsible AI and the challenges of AI systems going very right
  • The emerging field of AI consciousness
  • The Chief AI Officer: why you need one and the prerequisites for success  


For more information about the new research organization focused on AI consciousness co-founded by Daniel Hulme see conscium.com and his interview on the London Futurists Podcast.
Trust and faster AI time to value in manufacturing at IFF24 Jul 202400:42:07
How do you deliver impact with AI and ML and cut development time by weeks and even months? By understanding your customer, building trust, and managing risk. 

Done well, effective and responsible AI practices can be the secret to faster implementation, adoption, and performance at lower cost and risk.

In this episode with Dr. Alex Manasson, Data Science Leader for the Americas at International Flavors and Fragrances (IFF), we uncover their best practices for managing risk and driving rapid AI development and adoption in the safety-focused world of manufacturing.. Dr. Manassof shares insights on balancing statistical process control with predictive modeling, the importance of adapting your data collection processes, and the pros and cons of digital twins. Discover practical tips and strategies for implementing AI and ML tools to boost efficiency and foster trust in high-stakes environments. 
How to Make Responsible AI Happen: A Historical View05 Jul 202400:28:24
How do you deliver value with responsible AI, who is responsible for it, how do you put it into practice, and could we use AI to make our organizations more ethical?  

This episode comes to you from the RevX conference in London, where we asked these questions of Chris Wiggins, Chief Data Scientist at the New York Times. He is also Professor of Applied Mathematics at Columbia University and author of the books “How Data Happened: A History from the Age of Reason to the Age of Algorithms” and “Data Science in Context”.

Join us as we discuss:
  • What we can learn from the history of research ethics and data legislation
  • The need for clear principles and defined ownership to ensure ethical AI
  • The translation of ethical principles into checklists, standards, and product decisions
  • The importance of benchmarking AI against human performance and addressing how human biases in data lead to biased AI outcomes

To see all of the sessions at the RevX conferences go to domino.ai/revx. 
The AI Race: Predictions for AI in National Security & the Public Sector19 Mar 202500:45:30

What does the global race for AI look like? How has the US Department of Defense been adopting AI by engaging the private sector? And how should we expect AI policy to shift under the new administration?  

In this episode – the last in our series on the future of AI – guest host Joel Meyer, President of Public Sector at Domino, sits down with General Jack Shanahan (Ret.) former Director of the Joint Artificial Intelligence Center at the U.S. Department of Defense, and Lauren Bedula, Managing Director at Beacon Global Strategies.

Join us as we explore:

  • How the U.S. Department of Defense is accelerating AI adoption
  • The global AI race: U.S., China, and allies
  • AI policy shifts under the new U.S. administration
  • Predictions for AI in the Public Sector for 2025 and beyond

Also, hear more of Lauren’s thoughts on national security and tech innovation on the Building the Base podcast.

Efficient Data Pipelines for AI and a Healthier World19 Jun 202400:33:30
AI is not all about the data, however, your ability to develop and deploy efficient data pipelines is absolutely critical for unlocking the power of AI at scale. But how do you manage modern data pipelines for AI and how do you deal with fragmented ecosystems and spiraling costs? 

In this episode, brought to you from the RevX Philadelphia conference,  Richard Swakla, AI/ML Specialist at NetApp, joins us to discuss the current trends and best practices in the life sciences around data and AI. 

Join us as we discuss:
  • The role of AI in enhancing productivity in healthcare and the life sciences, particularly in drug discovery,  claims processing and fraud detection.
  • The growing importance of hybrid cloud solutions to balance cost, efficiency, and infrastructure access.
  • Challenges in transitioning AI projects from pilots to production due to high costs and rapidly evolving models.


To see all of the sessions at the RevX conferences go to domino.ai/revx. 
Enabling AI on Enormous Financial Datasets at FINRA31 May 202400:27:18
How do you enable AI, data science and analytics on petabyte-scale data, with extremely stringent privacy and security requirements?

This episode comes to you from the RevX-New York conference where we had a fireside chat with Ivan Black - Director in charge of ML, AI, and analytics platforms at the US financial services regulator FINRA. 

Join us as we discuss:
  • The challenges of enabling AI on massive, rapidly growing financial datasets
  • Talent strategies to support the rapidly changing AI ecosystem
  • The importance of AI governance and reproducibility
  • Managing cloud costs


To see all of the sessions at the RevX conferences or to find information about attending upcoming ones go to domino.ai/revx.
Developing a Strategy for AI Transformation at Zendesk16 May 202400:38:14
How do you craft and implement a strategy to transform an organization with AI? Not just to build a growing portfolio of successful AI projects, but to fundamentally re-engineer the organization’s core processes, to radically increase productivity, to overhaul the company’s tech stack, and to prepare it for a future of AI-driven competition.

In this episode, Akshaya Murthy, who leads the AI efforts for Operations at Zendesk joins us to discuss the mandate and toolkit of the AI transformation leader, the importance of strategy for AI impact, the AI transformation efforts at Zendesk, and their successes to date. 

Join us as we discuss:
  • The AI transformation leader: mandate and skillset 
  • Strategy: the misunderstood and frequently forgotten key to AI impact
  • GenAI: the transformation leader’s new best tool for rapid impact 
  • Process transformation: the goal of AI transformation

Surviving and Thriving as an AI Leader in a GenAI World02 May 202400:44:14
AI leaders. Why do we need them? How do you become one? And above all, how do you keep your job as one?

In this episode, we are joined by guest speaker Mike Gualtieri, VP and Principal Analyst at Forrester, and we unpack the opportunities, pitfalls, and best practices of the AI leader role. He shares the pivotal role of AI leaders in catalyzing organizational transformation, their unique skill set that must encompass data science, business acumen, and software engineering, their importance in navigating the evolving regulatory landscape surrounding AI, and the need for platforms to facilitate rigorous auditing and compliance measures to foster trust and transparency.

Join us as we discuss:
  • The strategic imperative for AI leaders to curate a diversified portfolio of AI initiatives 
  • The multifaceted nature of AI risk management, spanning legal, ethical, and societal dimensions
  • The formidable challenges inherent in navigating and enforcing AI-centric regulations amidst rapid technological advancement

Unlocking the Disruptive Potential of Generative AI: A VC Perspective10 Apr 202400:29:21
GenAI is evolving at a breakneck pace, matched only by the startups that are looking to commercialize it. So what better way to understand the latest GenAI trends than to ask a venture capitalist specializing in AI? 

In this episode, we speak with James Cham, partner at Bloomberg Beta, about the state of GenAI – where it is delivering value today – and the challenges preventing firms from moving from incremental GenAI-driven productivity gains, to truly disruptive GenAI use cases. Along the way, we cover the problem of treating GenAI like software development, the rapidly changing economics of GenAI, and the key to success with all types of AI which is – as always – understanding people. 

Join us as we discuss:
  • The cultural challenges preventing companies from unlocking the disruptive potential of GenAI
  • Why developers don’t get data-powered applications (and why data scientists need product thinking) 
  • How GenAI can change our engagement with technology (by killing the GUI)

Overcoming the Data Challenges of AI-driven Drug Discovery27 Mar 202400:37:22
A human being consists of billions of cells, each with the same genetic code but interacting in a myriad ways that can eventually translate into disease. Understanding and treating that disease is, in essence, a data problem. But how do you unlock that data and how do you change an organization to systematically use that data to improve decision-making and accelerate drug discovery? 

In this episode, we speak with Volodimir Olexiouk, Director of Scientific Engagement and Data Science Team Lead at BioLizard, about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences. 

Join us as we discuss:
  • The challenges in discerning correlation from causation and integrating domain expertise
  • How bridging expertise gaps and merging data silos in pharmaceutical companies radically improves drug-discovery processes 
  • The promise AI holds for swifter and more effective responses to future pandemics

AI Will Plan Your Next Vacation: GenAI at Tripadvisor13 Mar 202400:30:44
Trip planning may well be the perfect AI use case. Too much information, too many combinations, and too little time —for humans, but not for Tripadvisor’s AI Trips. In this episode Rahul Todkar, VP Head of Data and AI, shares the secrets to building a trusted GenAI solution at internet scale and discusses the similarities and differences between data leadership roles at digitally native companies and more traditional enterprises.

Join us as we discuss:
  • How to use GenAI to unlock first party data
  • The ideal GenAI development team
  • The evolving role of data and AI leaders

From the Archive: A Hybrid Approach to Accelerating the Model Lifecycle28 Feb 202400:24:07
Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.

In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.
Unlocking AI in the Public Sector19 Feb 202400:31:31
What’s just as important as the government keeping us safe from AI? Government leveraging AI to keep us safe!

In this episode, we interview Joel Meyer – former head of strategy at the Department of Homeland Security (DHS) and the person who drove the creation of the DHS AI Task Force. Joel shares how they identified key areas where they could apply AI to improve national safety and security, such as combating fentanyl and child sexual exploitation and abuse, and the steps that the federal government is taking to build AI capabilities across the public sector.

Join us as we discuss:
  • Key areas where US government agencies are looking to leverage AI to improve mission effectiveness
  • The people, process, and technology steps that government agencies are implementing to scale AI and how they apply to the private sector
  • The importance and value of Responsible AI in public sector use cases and beyond


Disrupting Drug Discovery and Development With AI14 Feb 202400:38:45
There is no such thing as an AI drug, but AI and ML-models are driving the next wave of new treatments. In this episode, Brandon Allgood, Chief Data Officer at FogPharma and serial entrepreneur at the intersection of ML and Biopharma, shares his insights on how AI is disrupting the traditional process of drug discovery and development.

Join us as we discuss:
  • Why AI is so powerful for drug discovery
  • What data science needs to learn from engineering
  • How drug discovery processes need to be rebuilt with AI models at their core

The Future of Enterprise AI? AI in Production!05 Mar 202500:42:01
What does the near term future of Enterprise AI look like? A scramble to get valuable, cost-effective, enterprise-grade AI solutions into production, with rigorous governance at scale. If that sounds like a tall order - that’s because it is! However, large advanced organizations (yes, even outside of the tech sector) are already seeing success deploying AI solutions and we can expect the pace to accelerate rapidly in the next 1-2 years.

To find out how enterprises are gearing up for this challenge we speak with Richard Swakla, AI and ML Specialist at NetApp and get the inside scoop on the trends that are underway and the strategies organizations are putting in place to operationalize meaningful AI outcomes.  
Join us as we discuss:
  • The pressure to shift from experimentation to production
  • Breaking down the data, infrastructure and organizational silos
  • Best practices for tackling governance, security and cost challenges
  • Examples of AI efficiency gains in healthcare, pharma, and beyond

Mastering the Rare Art of ML Deployment31 Jan 202400:36:08
What’s the biggest problem in AI today? It’s that far too few projects make it to deployment. In this episode, Eric Siegel, founder of the long-running Machine Learning Week conference and creator of the first (and perhaps only) ML music video, tells us about his new book, The AI Playbook and the bizML framework for aligning stakeholders and maximizing the chance for deployment and impact.

Join us as we discuss:
  • Causes behind the high rates of AI project failure
  • Critical project steps for ensuring deployment
  • Humor as a means to bridge the gap in AI understanding


And check out:

Shattering the Myths of GenAI: Interview with Forrester Analyst Rowan Curran20 Jan 202400:42:14
The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.

Join us as we discuss:
  • The role of data scientists and ML engineers in GenAI projects
  • Successful approaches to prompt engineering
  • The linkages between MLOps and LLMOps

More Human Than Human? GenAI Customer Service at Bolt03 Jan 202400:28:43
Imagine Generative AI handling tens of thousands of conversations with your customers daily. Science fiction? Not at Bolt where this has been in production since the summer of 2023. In this episode, Mikhail Korolev – head of the data science team at Bolt’s food delivery service – shares the challenges and hard earned best practices for operationalizing a GenAI application that dramatically lowers cost while also increasing customer satisfaction.

Join us as we discuss:
  • How to leverage GenAI to automate customer service conversations
  • How to manage inconsistency and mitigate risk in GenAI apps
  • How to protect sensitive data and comply with regulatory requirements with GenAI

AI in 2024: Predictions on the Future of the AI Revolution20 Dec 202300:13:09
2023 has been an exciting year for AI, but it’s nothing in comparison to what we will see in 2024! Expect to see sensational successes amid the debris of projects that were set up for failure, a flowering of predictive AI, and the emergence of the scariest thing in AI to date (EU regulation). Tune in to this episode where Dr. Kjell Carlsson shares his top predictions for AI in 2024 and get ready for a year of scandal, fraud, plummeting processor prices, and ascendant AI leaders. Also, goodbye quantum computing!

Happy holidays from all of us at Domino Data Lab and the Data Science Leaders podcast.
The State and Future of Generative AI: Reflections on the Anniversary of ChatGPT with Anaconda CEO Peter Wang06 Dec 202300:45:36
ChatGPT wasn’t the beginning of generative AI, but it did spark the GenAI revolution. Now, one year since it was launched, how much progress have we made, what impact is GenAI delivering, what are the real risks, and what developments are just around the corner? Join this session with the titan of the data science community, Anaconda CEO Peter Wang, and Dr. Kjell Carlsson, Head of AI Strategy at Domino Data Lab, where we will cover:

  • The state of GenAI: where GenAI is delivering and missing expectations
  • The challenges: the real risks and remaining barriers to impact
  • The future: what advances are underway and what can we expect over the next year

CDOs: Changing the Operating Model for Data & AI Transformation22 Nov 202300:39:28
How do you achieve success as a Chief Data Officer? It is a role that is more important, yet more challenging, than it has ever been, with a rapidly expanding set of expectations from stakeholders in every part of the business.

Here to help us understand the CDO role, its evolution, and the keys to success is Gary Barr, Global Chief Data Officer at Legal & General Investment Management (LGIM). Drawing from his wealth of experience, Gary speaks about the three incarnations of the CDO – from data governance champion to air traffic-controller of AI-driven transformation – as well as the dangers of dividing teams into “offense” and "defense”, the goal of the data mesh, and why AI regulation should be welcomed, not feared.

Join us as we discuss:
  • The rapid evolution of the CDO mandate and its responsibilities
  • Changing the operating model for data and AI adoption
  • The importance of qualitative and sentimental measures of ROI

Transforming Education with Generative AI and Active Learning08 Nov 202300:41:58
Most experts agree that AI isn’t about replacing human intelligence, but about improving it. When it comes to education, we should take this literally.

In this episode we discuss how to use AI to transform how we learn with Stephen Kosslyn, President of Active Learning Sciences and Founder and Chief Academic Officer of Foundry College. Stephen brings unparalleled expertise when it comes to using AI in education from his remarkable career spanning leadership roles at Harvard, Stanford, and Minerva University, but also thanks to his recent book “Active Learning with AI: A Practical Guide”.

Join us as we discuss:
  • How Generative AI can make learning more effective and scalable
  • How to design educational programs, create training experiences, and assess student understanding using Generative AI
  • Overcoming the challenges of embracing AI in the education sector

For more on the science of active learning and detailed, practical Generative AI examples, please check out Stephen’s new book, available now.
“Lessons from the First GenAI Killer App"25 Oct 202300:46:09
How do you implement an enterprise-grade GenAI application that serves millions of users a day? By focusing your application and building the capabilities for operationalizing it at scale.

Join our upcoming fireside chat with Domino's SVP of Product, Chris Lauren, who will share lessons learned while operationalizing the world’s first enterprise-grade GenAI application to be used on a global scale, Github Copilot.

Join us to learn:
  • Success factors for GenAI use cases
  • Common challenges and how to avoid them
  • Key capabilities for operationalizing GenAI models at scale
  • Inferencing GenAI models cost-effectively

Honeywell: Delivering on the Power of Outlier Detection12 Oct 202300:16:42
Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?

To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.

Join us as we discuss:
  • How to spark a virtuous cycle with anomaly detection use cases
  • Driving continuous improvement by transitioning from unsupervised to supervised machine learning
  • Aligning the model development and software development lifecycles

Making Better Sustainability Decisions with AI27 Sep 202300:11:17
AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.

Join us as we discuss:
  • Overcoming the ESG data quality challenges with AI
  • Leveraging AI to contextualize data and drive consistency
  • How AI can provide greater transparency than human-only ratings

The Silent Future of AI in Financial Services19 Feb 202500:30:19
AI is transforming financial services and insurance, but rarely in the headline-grabbing ways you might expect. From intelligent process automation to fraud detection and risk management, AI is being embedded across operations, but as AI adoption grows, so do the challenges—cybercrime, rapid technological changes, and ever-evolving regulatory frameworks.

In this episode we sit down with Adam Gale, Field CTO for AI & Cyber Security at NetApp, and Mike Upchurch, VP of Strategy for Financial Services and Insurance at Domino, to explore:
  • The "silent revolution" of AI in finance
  • Unlocking the potential of unstructured data
  • AI governance for innovation and regulatory compliance
  • Strategies for driving AI adoption
  • Top predictions for AI in 2025 in FSI

Celebrity Guest Gregory Zuckerman: Trusting AI to Make the Decisions13 Sep 202300:12:41
How do you trust black-box AI models with decisions that will make-or-break your business?

This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems.

Join us as we discuss:
  • How trust in AI relies on trust in people and processes
  • The limits of explainability and transparency
  • The power of systems over stories

Solving the AI Talent Gap: Upskilling at Scale at Halliburton30 Aug 202300:45:46
Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.

Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.

In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.

Join us as we discuss:
  • How to upskill existing domain experts on data science methods
  • How to engage and drive alignment with corporate stakeholders through workshops
  • The benefits of upskilling domain experts on code-based data science tools
  • The importance of involving and upskilling leadership

The AI Innovator’s Dilemma: Insights from Harvard’s D^3 Institute16 Aug 202300:29:33
It’s been said: “When everything is important, nothing is important.” So how do you succeed with AI-driven transformation where everything – across people, process, and technology – is important? It requires leadership, a deliberate strategy, and ongoing organizational change.

Here to share insight on these transformational challenges and best-practices are Jen Stave and Catherine Feldman from the Digital, Data, and Design (D^3) Institute at Harvard. In this wide-ranging conversation, the duo draws upon seminal research from the Harvard Business School – such as professor Clay Christensen’s theory of Disruption – to explain how organizations must adapt their business and operating models, and make experimentation part of their organizational DNA.

Join us as we discuss:
  • Disruption and the reasons so many AI projects fail
  • The need for a holistic approach and strong leadership for AI success
  • Applying a jobs to be done” approach to generative AI


Also don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.
Get the Most Out of Generative AI02 Aug 202301:19:00
Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.

Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.

Join us as we discuss:
  • Separate generative AI facts and fiction
  • Take a closer look at AI applications you can start using today
  • Examine the future of AI and its impacts on the workforce and the workplace

Celebrity Guest Reid Blackman: Who’s Responsible for Responsible AI?19 Jul 202300:25:14
“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.

Join us as we discuss:
  • Why AI poses greater ethical risks than other technologies (and humans)
  • Leadership and the other key elements of a successful AI / digital ethics program
  • The importance of explainability

Output to Outcomes: AI Product Management at Verizon05 Jul 202300:43:27
When it comes to driving business impact with AI, there are no silver bullets, but data science product management comes pretty close. It could well be the key to bridging the gap between business and technical teams, designing solutions to meet the business need, spurring ideas from experimentation to implementation, and driving continuous improvement. But how do you build a product management capability for data science?

In this episode, Alek Liskov, Director of AI & Data Product Management at Verizon, shares their hard-won best practices in building data science product teams and their phenomenal successes in delivering AI-driven impact.
Join us as we discuss:
  • The emerging discipline of data science product management
  • How to build durable product teams for data science
  • Where to find and how to develop data science product managers

Celebrity Guest Steven Levy: AI, a mirror to human intelligence21 Jun 202300:22:24
What’s different about the AI wave today versus the 1980s and what do the latest advances reveal about our human intelligence? We’re behind the scenes at Rev4 with Steven Levy, best selling author and Editor at Large at WIRED. Steven shares insights he’s built over the past four decades writing about AI and the people (like Marvin Minskey) and companies (like Google and Facebook) that have brought us to where we are today.

Join us as we discuss:
  • A brief history of AI - from dashed hopes to triumphant transformers
  • How AI is helping us understand human intelligence
  • The regulatory risks of anthropomorphizing AI

Season 2: Host to Host07 Jun 202300:45:15
Who’s the best person to share the secrets of Data Science leaders? Try someone who has spent the last year interviewing them! Former industry analyst and new host of the podcast, Dr. Kjell Carlsson, interviews Dave Cole on all surprising things he’s learned in hosting the nearly 50 episodes of season 1.

The two delve into various topics, such as how you may unexpectedly become a data science leader, the experimental foundation of data science, the importance of data science leaders being a part of strategic conversations, and the hard-won best practices that have emerged. They also touch on the sales and marketing aspects of being a data science leader, and the evolving future of data science, and the data science leader.

Join as we discuss:
  • The unexpected origins of data science leaders
  • The emerging consensus on how to drive impact with data science
  • How and why data science is moving higher and higher in organizations
  • The future outlook for data science, and its leaders

What It Takes to Productize Next-Gen AI on a Global Scale (Srujana Kaddevarmuth, Senior Director of Data & Machine Learning Programs, Walmar31 May 202200:41:47
What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale?
Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale.
In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what’s next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field.
We discuss:
What to prioritize when experimenting with next-gen tech
How to use “communities of practice” to align your organization
Solving governance, reproducibility, and knowledge sharing challenges at scale
Bringing more women into data science 


In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science.
Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started.
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
Help Me Help You: Forging Productive Partnerships with Business Stakeholders (Sunil Kumar Vuppala, Director of Global Artificial Intelligenc12 Apr 202200:38:03
There’s tremendous value in pure data science research. In an enterprise context, however, it all comes down to how learnings and insights from that research can help advance business growth, customer experience, and product innovation.
Sunil Kumar Vuppala is the Director of the Global Artificial Intelligence Accelerator at Ericsson. His career journey from a researcher role to data science leadership has given him years of perspective on how ML professionals and their business side counterparts can build partnerships that pay off in both the near and long term.
In this episode, Sunil shares some of those key lessons on education, communication, and collaboration. Plus, he details a unique MLOps strategy he’s employed to address challenges with scaling model monitoring.
We discuss:
How a research background can inform leadership style
MLOps best practices for scale
Forming mutually beneficial partnerships between business stakeholders and data science teams



Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
Pharma Is the New Tech: The Future of AI in Life Sciences05 Feb 202500:32:14
AI is transforming drug discovery and clinical trials, making what was once cutting-edge the new standard. Pharma companies are evolving into tech companies, leveraging AI to revolutionize every phase of drug development.

In this episode, Chris McSpiritt, VP of Life Sciences Strategy at Domino, discusses how AI is no longer a niche capability for select firms but an essential capability for all biopharma organizations. We explore AI-based digital organisms, the increasing reliance on AI in clinical trials, and the shift toward personalized medicine.

Join us as we dive into:
  • How AI will drive the majority of new drug discoveries
  • The role of AI in clinical trials
  • The growing need for pharma companies to adopt a tech company mindset
  • The impact of AI-driven automation on regulatory compliance and reporting

Change Management Strategies for Data & Analytics Transformations (Michal Levitzky Head of Data & Analytics - CDO, Migdal Group)05 Apr 202200:39:24
Large enterprises will always have some internal groups that are more change-averse than others. But progress often necessitates change, and how well you navigate the change management process can make or break your success as a leader.
Michal Levitzky is the Head of Data & Analytics (CDO) at Migdal Group, a leading insurance and finance company in Israel. Michal has spearheaded the introduction of data and analytics functions at multiple organizations, and she knows a thing or two about negotiating the complexities of change management during analytics transformations.
In this episode, Michal shares her advice for AI leaders driving meaningful change at their own companies. Plus she details her philosophy on structuring data and analytics teams for maximum efficiency and collaboration.
We discuss:
Using experience in fields like accounting as building blocks for leadership in data science
Change management during model-driven transformations
A structure to enable BI and data science functions to better support each other 


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
A Hybrid Approach to Accelerating the Model Lifecycle (David Von Dollen, Head of AI, Volkswagen of America)29 Mar 202200:23:55
Without a clearly defined methodology, complex projects with multiple technical and business stakeholders often fall apart. The risk is especially high when trying to scale data science work in an enterprise organization. 
That’s why David Von Dollen, Head of AI at Volkswagen of America, integrated agile methodology with CRISP-DM to help his team navigate roadblocks and accelerate progress on the path to model deployment. He shares how this hybrid approach enables his team to be more strategic about project lifecycles, unlocking real business impact even faster. 
Plus, David provides advice for building relationships with key business stakeholders and shares his philosophy on using the art of data science to benefit humanity. 
We discuss:
Implementing hybrid CRISP-DM and agile methodologies
Building relationships with stakeholders across the business
Using data science to solve challenges outside of work      


Mentioned during the show:
DataKind     


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
Giving Back and Building Your Brand as a Data Science Leader (Sidney Madison Prescott, Global Head of Intelligent Automation - RPA, AI, ML,22 Mar 202200:31:49
Even with the recent rise of specialized data science degree programs, top-notch data science talent can come from anywhere. 
Those in leadership positions have a duty to share their knowledge and support aspiring data scientists, regardless of the unique path that brought them to the field. 
Sidney Madison Prescott, Global Head of Intelligent Automation (RPA, AI, ML) at Spotify, has made a habit of sharing her expertise and giving back. And in the process, she’s built a personal brand that would inspire future leaders in any industry. 
In this episode, Sidney shared her career story, offered advice for building diverse data science teams, and detailed her work in robotic process automation at Spotify. 
We discuss:
Sidney’s career journey and her guidance for women and people of color in data science
How a strong personal brand can open doors to opportunities in tech
Why data science leaders should care about robotic process automation  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
Governing Models and Structuring Teams in Highly Regulated Industries (Anju Gupta, VP Data Science & Analytics, Northwestern Mutual)15 Mar 202200:30:42
Model governance is vital, especially in heavily regulated industries like insurance.
Strong governance can help ensure that key models are reproducible, explainable, and auditable—all important factors for both internal model development workflows and for external regulatory compliance. But the best governance strategy isn’t always obvious.
Anju Gupta, VP Data Science & Analytics at Northwestern Mutual, is a big believer in establishing model governance practices early, and she shares her thoughts on the topic in the episode. Plus, she talks about some surprising roles on her data science team and the unique value that comes from pairing actuaries with data scientists.
We discuss:
How to establish scalable model governance practices
The intersection of actuarial work and machine learning
Roles you didn’t know you needed on your data science team


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
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