Management in AI

Career Paths of AI Leaders

June 2, 2022
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Being a leader in one's industry is no easy task in the field of AI. It requires alignment with the organization and, as a leader, the ability to blaze the trail for the organization.

Over the recent years, Artificial intelligence has become an increasingly widespread and growing form of technology. The finance, technology, and healthcare industry are currently leading the charts in growing their AI divisions. Still, the retail, food & beverage, and real estate industry are seeing value in AI. The rising variety of sectors indicates the importance of investing in AI to sustain competitive advantage.

From our latest release of the top 100 fastest-growing AI teams, we have selected a handful of leaders from the top AI teams and will examine the career journeys that landed them where they are now.

Let's explore who is leading the fastest-growing AI teams!

1. EXL
EXL Service is a global analytics and digital solutions company that tailors data solutions so companies can make better business decisions and drive more intelligence into digital operations. The AI team at EXL has seen exceptional growth over the past three months.

Smita Sawant, VP of Healthcare Data & Analytics
With a demonstrated track record of leading teams, Sawant has worked in 5 different companies and has swiftly transitioned into leading teams. Today, she has more than ten years of leadership experience. Her experience spans from teams of business analysts and product owners to cross-functional teams, and she now serves as the VP of Healthcare Data & Analytics at EXL.

2. Pizza Hut
Our second pick is Pizza Hut, the American multinational restaurant chain, and international franchise. With more than 18,000 restaurants worldwide, it has more than doubled its AI team over the past three months (U.S.). There is no question that Pizza hut has become an industry leader in this area.

Tristan Burns, Global Head of Analytics
Burns is leading the AI journey that Pizza Hut has embarked on. Their AI practice combines customer behavior with knowledge about who customers are with their geographic location to drive customer recommendations. Burns gained experience as an individual contributor at Bank of America. He delivered a suite of analytics reports to executive management and later served as a BI analyst to develop a tech startup.

3. Ulta Beauty
The next featured company is the largest beauty retailer in the U.S., with products and services ranging from cosmetics, fragrance, skincare products, hair care products, and salon services. Ulta is a firm believer that technology is the critical driver for the future of cosmetics. It is known for its AI-driven approach with customer products like Beauty Advisor, Skin Analysis, and GLAMlab Virtual Try-On. In our latest release of the top 100 fastest growing AI teams, Ulta ranks highest among the retail companies.

Kedar Pandit, Vice President of Strategic Analytics & Data Science
With a background in consulting, Pandit has experience in solving high-impact business problems across various industries, including retail, pharmaceuticals, airlines, and telecom. He has worked in several analytics positions in McDonald's and Crate and Barrel, where he delivered data-driven customer engagement tactics. He now serves as Ulta's VP of Strategic Analytics and Data Science.

4. EPAM Systems
Our fifth pick is the technology company Epam, specializing in service development and digital platform engineering beyond just running analytics reports. Epam delivers company solutions that glean actionable information from their data and support data products across product portfolios. It is one of the world's largest manufacturers of custom software and consulting providers.

Anton Petrov, Chief Data Scientist at EPAM Systems
Petrov is a Data Scientist with 15+ years of experience in converting data to value with data science, machine learning, and AI. He served as an individual contributor as a data scientist for six years before leading small data science teams. Today, Petrov is leading the data science team at EPAM and has worked in various domains, including healthcare, insurance, finance, retail, and telecom.

These AI leaders demonstrate that data professionals come from diverse backgrounds and don't always follow the same career upbringing.

The Variant Journey in Becoming a Leader in AI
To understand the journey deeper, we spoke with our brand-new Head of Data Science, Justin Norman, to share his perspective.

Justin is the former Vice-President of Data Science at Yelp and led data science and machine learning teams at Cloudera, Fitbit, and Cisco. Justin also proudly served in the United States Marine Corps.

Justin explained how a few themes have been recurrent over his career.

First, it's often helpful to have some technical individual contributor time before transitioning into leadership. Norman shares two reasons for this. ML/AI leaders are often responsible for or primary customers of data science software, platforms, and infrastructure. Knowing some basics of what the work looks like in practice is key to making sound decisions and recommendations. Another aspect is the need for leaders to understand, respect, and support the work of those they work with on a team, and understanding where they struggle and what makes them most successful is imperative.

Another theme involves holding cross-functional roles outside of the data realm and using that to enrich your capabilities as a data leader: "For me, that meant holding program and product manager roles, managing a P&L, and participating in the sales cycle," Norman said.

He further explained that as one gets more senior, these skills are still a component of the job, even if your title says Director or VP.

Finally, building and maintaining a community of practice is another vital aspect, no matter the seniority. "Often, when I'm stuck on a tough problem, I reach out to people I know who are sharp in the area and try to talk it out with them. These people often have the best talent, tools, and technique recommendations. ML/AI/ DS is a highly collaborative field! When the tech fails, often the people can pick up the slack." - Justin Norman.

The roadmap to becoming a leader in AI isn't a monolith. AI professionals come from all sorts of backgrounds, indicating no one size fits all.

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