Data Science Leaders

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.

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.

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 industries
Operationalizing AI and measuring the ROI
Strategic recommendations for data science leaders
AI’s contribution to the fight against COVID-19


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.

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 manufacturing
Strategies for sharing data to improve manufacturing processes
Human-in-the-loop applications
Looking 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 strategy
Using models to drive efficiency across the organization
The 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.

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 chain
How an apprenticeship model can bring data scientists closer to the business
Unique hiring strategies in an ultra-competitive market


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.

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 products
Managing globally distributed data science teams
Understanding human intention and well-being with technology


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.

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 healthcare
Managing bias in healthcare data sets and models
Motivating 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 journey
How to prioritize data projects for a larger impact on revenue


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.

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 today
Why data science leaders should care about responsible AI
The ingredients for an effective data science consulting practice
ROI 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.