Past Event

SCALE: SF Data Science Leaders

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

Head of Data @ Strava
Shailvi is a data analytics leader with 16 years of experience in Fortune 500 companies and startups.

8+ years in management leading global analytics & engineering teams across USA, Europe and Asia.

Shyam Banuprakash

Former SVP of Data and Insights @ The New York Times
Shyam has been in the life sciences industry for more than 10 years and in data science for around 15 years.

He is the VP of data science at Clario where he works in the medical imaging business segment and manages a team of 20 data scientists across the globe.

Remy Thellier

Head of Growth @ Vectice
Remy previously led the growth of Syntony GNSS. He is a former French diplomat specializing in the deployment of Deep Tech / AI companies in the USA.
“We are staffed well enough that we can avoid conflict between priorities for science and non-science efforts.”
“Having the CFO as an ally for data science projects is great for data leaders. Part of that is ensuring that the CFO sees the return on investment by focusing on different parts of the life cycle.”
“Having centralized learning and building and a space where the whole team can gather and work on something together is important. That’s what we did in our team.”
“When there are multiple tasks, we leverage experts to prioritize what we're working on. We have experts in the data science and medical teams that collaborate to decide what’s most important to work on.”
“It's easier to get someone who knows medical science and train them on data science than the other way around. Their domain knowledge comes in handy when we’re trying to prioritize the workload.”
“If you're onboarding talent from outside, you must do due diligence before starting the hiring process. This includes defining the role clearly and how it fits into the team, think about career trajectories, and design a smooth onboarding procedure to reduce training time.”
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SCALE: SF Data Science Leaders

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About the event

SCALE: SF Data Science Leaders was the event exclusively for Senior Directors and VPs of data science leaders. We maintain and grow a community of leaders in the field. We want to foster collaboration and conversations between our members. We assembled leaders from various industries, from startups to large corporations, to share their challenges while managing data science teams at scale.

Two prominent leaders in the data science field from Clario & Strava shared their valuable insights on how to:
- Best Approach to C-suite buy-in
- Foster diverse teams
- Leading remote teams in AI
- Successful Collaboration with domain experts

Thank you for joining us!

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

Leadership Insights

We invited two attendees on stage for a fireside chat: Shyam Banuprakash (Clario's Vice-President of Data Science) who talked about successful collaborations with domain experts. Shailvi Wakhlu (Strava's Head of Data) discussed getting C-suite buy-in and building diverse teams.

They generously shared their management struggles with the audience and tips on how to solve them:

Building Trust in the Organization
Shyam Banuprakash, Vice President of Data Science @ Clario: "Building trust is very important. One approach is to avoid complex models, at least in the beginning." Shyam revealed one way to build trust among teams is to start by using a simpler model for which the outputs can be easily explained and then build off of that"

The Challenge of C-Suite Buy-In
Shailvi Wakhlu (Strava's Head of Data): "Getting buy-in from executives is about understanding everybody's motivations. CFOs focus on the business ROI, while CMOs have marketing budgets and want to show how those initiatives also bring value to the company." Shailvi explained how the key to executive buy-in is to help every person look better in their role.

Vectice Introduces Auto-Documentation Solution for ML Projects & Their Governance

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