Past Event

Voices of
ML Leaders

Align Data Science with Business Goals, Hiring & Retaining Your Data Talent, Centralized vs. Embedded ML Teams
San Francisco, CA
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Dr. Ragnar Lesch

Head of Data Science @ Kohl’s
Formerly leading the team of data scientists focusing on Kohl’s business cases such as price and assortment optimization, inventory allocation, the Covid impact on sales, outlier detection, and demand forecasting.

Pushpraj Shukla

Partner Director AI & ML @ Microsoft
Formerly leading one of the most prominent applied AI/ML groups in Azure. We build machine learning models that power the low/no-code insights offered through many Business Apps from Microsoft.

Justin Norman

Former Head of Data Science @ Yelp
Experience leading 5 data science teams at Yelp, the corporate Data Product Management team, the Yelp DS/ML experimentation platform (Bunsen), and the Core Machine Learning Platform.
“Data science talent is hard to find. Sometimes, to retain the best people, you have to let them work from wherever works best for them and their families.”
“To build trust, we realized we had to explain better what we were doing and why we were doing it.”
“We want to learn how they think about a data science task, and not just talk about the previous models they have built(...) We have done this for 18 months and, so far, have not had a bad hire.”
“The key is to rebuild the job description together and spend more time telling the team's story. We created and modified different individual career paths and opportunities instead of just one data science career path for everyone.”
“We emphasize the mission behind the job. We don't just sit in the dark behind our models. We go and speak to the customers who use our models, and we share the success storiesand the failure stories.”
“The most important thing, from a business alignment standpoint, is to get clarity on what the core metrics are and to get alignment on how to actually measure them. Deciding that is a leadership responsibility, and you can't shirk it.”
Get the key insights from the event

Voices of ML Leaders


About the event

Voices of ML Leaders brought data science leaders together to discuss talent retention, talent acquisition, aligning AI initiatives across business units, and building trust between data science teams.

Industry experts from Microsoft, Kohl's, and Yelp joined for a fireside chat and shared their experiences in the data science field and strategies for successfully leading teams. Attendees learned best practices to build trust, create an effective team structure, and leverage AI initiatives to solve business challenges.

Thank you for joining us!

25+ companies leading in data science joined us for our event.

Leadership Insights

The panelists discussed the importance of investing in talent and creating a workplace where data scientists can thrive. They emphasized that data science is an analytical tool and a cultural transformation that needs to be supported from the top down. They suggested proactive steps such as what it takes to hire with so many people opting to work remotely, building trust in the organization, and aligning the business with its AI initiatives.

Talent Acquisition & Retention
The speakers agreed: Data science talent is hard to find. Recruiting and retaining data science talent can be a challenge. To ensure the best possible results, businesses should provide flexible working arrangements for their employees. This could allow teams to work remotely or from different locations when needed. By providing such flexibility, employers demonstrate that they value their people, which helps to foster loyalty and increase employee retention rates

You also have to create an environment that allows data scientists to experiment and be creative. They need to be able to explore, learn and push boundaries. Giving them the right tools and access to resources is also essential.

Align Business with AI Initiatives
Businesses must align their initiatives with their AI strategies. Without this alignment, companies may miss out on potential opportunities or, worst case, lose competitive advantage. The panelists suggested that data scientists be involved in setting goals and objectives and helping to ensure that all stakeholders are on the same page and understand each other's roles.

The panel concluded by emphasizing the importance of investing in talent and creating a workplace where data scientists can thrive, setting clear goals and objectives for AI initiatives, and building trust among teams and stakeholders.

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