7 Top Innovators Share Insights, Trends and Career Advice in 'The Data Science Innovator’s Playbook'

Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business?

These questions—and many more—are the focus of “The Data Science Innovator’s Playbook,” a free Domino Data Lab ebook that explores the work, ideas, and experiences of seven people whose work is revolutionizing data science and business, and having an impact on some of the world’s biggest problems.

The ebook profiles:

• Cassie Kozyrkov—Chief Decision Scientist, Google
• Robert Nishihara—Co-creator of Ray, and Co-founder & CEO, Anyscale
• Mona G. Flores—Global Head of Medical AI at NVIDIA
• Glenn Hofmann—Chief Analytics Officer, New York Life Insurance Company
• John K. Thompson—Analytics Thought Leader, Best-selling Author, Innovator in Data & Analytics
• Andy Nicholls—Senior Director, Head of Statistical Data Sciences, GSK plc

What You Can Learn from These 7 Top Innovators

More than 150 attendees took our survey at Domino’s Rev 3 MLOps conference, writing in the names of people they thought were top data science innovators.

In our interviews, we asked these six data scientists—and a medical doctor doing groundbreaking work in federated learning and medical AI—about everything from the rise of data science, to where new breakthroughs might come from in the future, to career advice for aspiring innovators. Their answers are insightful, inspiring, and sometimes even surprising.

All say that better tools for data scientists are a key driver of innovation. As Google’s chief decision scientist, Cassie Kozyrkov, puts it: “The world is finally on board with understanding that ‘knowledge is power’ belongs in the same bucket of ideas as ‘data is useful.’”

"And if data is useful, then data scientists are useful, and their work needs to be streamlined to extract value and accelerate innovation,” says Kozyrkov.

Data Science Fuels Search for Solutions to the World’s Most Important Problems

In pharmaceuticals and medicine, for example, AI models are no longer confined to the chemistry of a cure, but are used to investigate all facets of healthcare delivery.

And as Mona Flores, Global Head of Medical AI at NVIDIA, puts it, “There is a lot of opportunity to apply AI to the business of healthcare, because predicting and analyzing needs ahead of time can be critical to the work of doctors, nurses, and other caregivers.”

Andy Nicholls, senior director and head of statistical data sciences at GSK plc, agrees that data science is driving many more facets of the business these days than in previous years. Just one example is streamlining the approval process for new drugs and treatments.

“For us, data has an impact in the approval process, where we have to prove that our drugs work and are safe,” says Nicholls.

That was part of the motivation for Nicholls’ involvement in advancing the Pharmaverse, which you can learn more about in the ebook.

Better Tools Advance Data Science Innovation

New tools and new applications for data science have made many fields more innovative, including finance and insurance.

“Both the infrastructure and software to deploy models into production have vastly improved, and that drives the impact our models can have on the business,” says Glenn Hofmann, chief analytics officer for New York Life.

Even more intriguing, the meteoric rise of data science might only accelerate with easier access to distributed data and resources.

Nishihara: Data Science’s Impact Will Only Accelerate with Distributed Computing

“Today, to do AI and data science, you have to build a lot of infrastructure for scaling,” says Robert Nishihara, co-inventor of Ray and CEO of Anyscale. “That’s what we’re working on with Ray and Anyscale.”

Read more from Nishihara on the future of Ray and the role of distributed AI and ML in data science innovation.

How Is the Profession Changing? Career Advice from the Top

Everyone acknowledged that there will be changes ahead for the profession and offered helpful advice for rising data scientists. John K. Thompson, best-selling author and thought leader on managing data science teams, was especially forthcoming, and minced no words.

“Don’t work for jerks,” he says. “And one of my MBA professors taught us that what you become at work will have a direct and dramatic impact on your personal life. The two cannot be separated. If you are rude and unbearable at work, you’ll be that way in your personal life.”

For more frank and insightful career advice, as well as insights about the profession and its impact on innovation, check out the ebook here.