Data Science Leaders

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.We discuss:Five elements of the data science and analytics value chainHow an apprenticeship model can bring data scientists closer to the businessUnique hiring strategies in an ultra-competitive marketTune 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.

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 focusing on well-being when it comes to how brands relate to customers. He joins the show to share his perspective on the future of data science, plus he details his approach to managing a large team spanning many products, cultures, and geographies.In this episode, we discuss:The role of ML in unifying the customer experience across multiple productsManaging globally distributed data science teamsUnderstanding human intention and well-being with technologyTune 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.

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 his perspective on bias, plus he details his approach to fostering employee motivation and positive team morale.In this episode, we discuss:Problem-solving in data science and healthcareManaging bias in healthcare data sets and modelsMotivating high-performing employees and 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.

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, Accenture, Yahoo!, eBay, and more. She joins the show to discuss the meaning of autonomous enterprise and the process required for true transformation.We discuss:What is an autonomous enterprise?Where are companies falling short in their data transformation?The investment and first steps required on the transformation journeyHow to prioritize data projects for a larger impact on revenueTune 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.

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 consideration for every data science team. Plus, he explains what you need to be successful in AI consulting, and why a portfolio approach to ROI is the best way to demonstrate value to the business. We discuss:The difference between AI in the 1980s and todayWhy data science leaders should care about responsible AIThe ingredients for an effective data science consulting practiceROI analysis 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.

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 global supply chain crisis impacts sectors from grocery to apparel.Plus, she explains why two things are so vital to the success of a data science team: ML engineers and a culture of communication.We discuss:How data science teams are navigating the supply chain crisisThe vital role of an ML engineerTips for communicating about data science in businessTune 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.

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 science can help address inside any organization.We discuss:The role of advanced analytics in the legal sphereUse cases on both the business and practice sides of lawHow analytics leaders and general counsels can work togetherWhat’s next in the world of legal analytics  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.

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 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it’s him.In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store.We discuss:- Managing large data science teams at scale- Making time to gain knowledge from the ML community- What a feature store is and why data scientists should careMentioned during the podcast:- The Idealcast with Gene Kim- Mik + One with Mik Kersten- a16z Podcast- Yannic Kilcher on YouTubeTune 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.

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 this episode, Chun Schiros, SVP, Head of Enterprise Data Science Group at Regions Bank, reveals how her team is leveraging AI solutions to optimize the banking experience. And with insight applicable to data science leaders in any industry, she shares her change management tips for driving adoption of machine learning among data skeptics.We discuss:- How data science use cases have evolved in the banking industry- AI solutions in banking that optimize the customer experience- Change management tips for winning over data science skepticsTune 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.

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 deciding whether to patent an algorithmic process—and what benefits it can bring. Plus, he talks about why a statistical background is so important for teams that generate data.We discuss:- The transition from getting a PhD in chemistry to the analytics world- Finding the balance between statistical and computer science backgrounds- Why you should patent your data science work and how to do itTune 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.