Data Science Leaders

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 leadersThe emerging consensus on how to drive impact with data scienceHow and why data science is moving higher and higher in organizationsThe future outlook for data science, and its leaders

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 techHow to use “communities of practice” to align your organizationSolving governance, reproducibility, and knowledge sharing challenges at scaleBringing 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.

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 styleMLOps best practices for scaleForming mutually beneficial partnerships between business stakeholders and data science teamsTune 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.

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 scienceChange management during model-driven transformationsA 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.

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 methodologiesBuilding relationships with stakeholders across the businessUsing 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.

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 scienceHow a strong personal brand can open doors to opportunities in techWhy 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.

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 practicesThe intersection of actuarial work and machine learningRoles you didn’t know you needed on your data science teamTune 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.

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 can operationalize AI at life sciences companies.Plus, we talk about the role data science has played in the fight against COVID-19 and the remarkable effort to develop such highly effective vaccines.We discuss:How data science in life sciences compares to other industriesOperationalizing AI and measuring the ROIStrategic recommendations for data science leadersAI’s contribution to the fight against COVID-19Tune 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.

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 impact that ML professionals in any field can learn from.In this episode, Alon Malki, Senior Director of Data Science at NI (National Instruments), opens a window into the world of data science in electronics manufacturing. Plus, he shares why human-in-the-loop processes are essential to gaining buy-in for AI in the enterprise.We discuss:Data science in electronics manufacturingStrategies for sharing data to improve manufacturing processesHuman-in-the-loop applicationsLooking for challenge-motivated data science talent  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.

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 Science, AI & Product Insights at DocuSign, explains how to create strong business partnerships to earn data science a critical and strategic seat at the table.Plus, he shares his unique perspective on the business impact of models and why self-service tools are essential to delivering value.We discuss:How to use data science to inform business strategyUsing models to drive efficiency across the organizationThe role of self-serve tools in data science 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.