Data Science Leaders

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 broug...

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 lea...

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...

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 leader...

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 o...

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...

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...

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...

In every industry, people consume data. They work to understand what it can tell them in order to make smarter decisions. But the nature of data in the world of life sciences presents some unique challenges—and opportunities—for data science. In this episode, Sidd Bhattacharya, Director of Healthcare Analytics & AI at PwC, dives deep into these dynamics and shares his perspective on how leaders ca...

Many people assume that once you establish a manufacturing line, the hard work is done and things remain relatively static. The reality, especially in electronics manufacturing, is entirely different. Constantly changing data streams and endlessly dynamic variables present some unique challenges for data scientists in the field. But there are lessons on data sharing, model adoption, and real-time...

When your data science team is consistently more reactive than proactive in addressing business challenges, it can be difficult to be seen as strategic partners. But by prioritizing building business domain expertise and always asking about the “why” behind any request, you’ll start to build a rapport and change the nature of the relationship. In this episode, Indy Mondal, Senior Director of Data...

To create sustainable business value, data scientists need to navigate all the elements of what this episode’s guest has dubbed “the data science and analytics value chain.” So what are those elements? And how can you ensure you hire and develop the team that delivers on each one with every single data science project? Nancy Hersh, Chief Data Officer at Arcadia, joins the show to break it all down...

The coding, models, and experiments inherent in data science work may have more to do with understanding human well-being than you think. Machine learning and AI can be applied in ways big and small to further our understanding of human behavior—and influence our well-being. Takuya Kitagawa, Chief Data Officer & Managing Executive Officer at Rakuten Group, believes there must be a shift toward foc...

Bias is an ever-present enemy of sound data science in healthcare. Without proactive measures to mitigate bias in the data used to build and train models, real people can bear the brunt of potentially life-altering negative consequences. Vikram Bandugula, Senior Director of Data Science at Anthem, knows this issue intimately from his extensive experience in healthcare. He joins the show to share h...

Is your team mining all available data to inform your business strategy and grow revenue? Is your company prepared to compete against others who are? If you’re like most, the answer is probably no. How can you future-proof your organization and take steps toward an autonomous enterprise? Janet George is an enterprise AI leader and author with experience across companies including Oracle, Apple, Ac...

Who uses the models that we create and how do they use them? Those key questions underpin the notion of responsible AI.  Since algorithms can have a significant societal impact, it’s vital that data scientists are aware of the broader context in which they may be applied.  In this episode, Anand Rao, Global Artificial Intelligence Lead at PwC, breaks down why responsible AI should be an important...

When highly disruptive events like the COVID-19 pandemic occur, data science teams may have to throw historical data out the window. Models trained on what happened in the past simply don’t work in a radically different present. In this episode, Karin Chu, VP Data Science and Digital Analytics at Peapod Digital Labs, discusses how her team is tackling that challenge head on, particularly as the gl...

Legal work may not be an obvious application of data science to many advanced analytics leaders. But that should change. In this episode, Peter Geovanes, Head of Data Strategy, AI & Analytics at Winston & Strawn, breaks down the nuts and bolts of legal analytics and how it’s revolutionizing the way law firms win new business—and cases. Plus, he shares insight on the types of legal challenges data...

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day? Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 dat...

As compute capability continues to expand, the banking industry is turning more and more to data science to enable better customer experiences. Use cases have proliferated, from product recommendation engines to predictive customer retention alerts. These innovations can drive real business value, but managing the rollout of process and technology changes always presents interesting challenges. In...

Should your team patent its data science work? With open source such an important part of the data science community, patents almost seem antithetical to the ethos of the field itself. But it turns out, there are some very good reasons to pursue data science patents in business. In this episode, Kli Pappas, Associate Director of Global Analytics at Colgate-Palmolive, shares his team's process for...

There are many ways to structure a data science function in a global enterprise. But what’s been the winning strategy for global technology distributor Ingram Micro? Creating a data science “nerve center.” Centralizing data science talent has helped elevate analytics at Ingram Micro to better solve complex business problems using machine learning and AI. In this episode, Tim Suhling, VP Global Bus...

To embed models into SaaS platforms at scale, it pays to have a cross-functional team—software engineers, UX designers, data scientists, machine learning engineers—all working together. That collaboration allows you to tackle hard challenges around scaling models to work across hundreds of thousands of customers. And it enables you to build something that offers tremendous value across many differ...

In healthcare, only 14% of scientific discoveries actually make it into clinical practice. But data science, in lockstep with the digital transformation, is helping to change that. As healthcare data and clinical studies transition to digital form, the opportunity to use data science and AI to generate insights and recommend treatment pathways is greater than ever. And the ability to make healthca...

As business leaders become more educated on the value that machine learning can deliver, the demands on data science teams only become greater. Business stakeholders are now interested in much more than the accuracy of predictive models. They’re asking questions about productionization, scalability, and bottom line ROI. In this episode, Nimit Jain, Head of Data Science at Novartis, joins the show...