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Jessica Lachs is the global head of analytics and data science at DoorDash, where she’s built one of the largest and most respected data organizations in tech. In her more than 10 years at DoorDash, she has served as the first general manager, responsible for launching new markets; the head of business ops and analytics; and the VP of analytics and data science. Previously, Jessica founded GiftSimple, a social gifting startup, and started her career at Lehman Brothers as an investment banking analyst. In our conversation, she shares:
How to structure and scale a high-impact analytics organization
Centralized vs. decentralized data teams
How to pick the right metric and aligning incentives
Advice for data people on how and when to push back
Lessons learned from building a global data team
How to foster a culture of extreme ownership
The role of AI in improving analytics team productivity
Advice for aspiring data leaders without formal training
Listen now on Apple, Spotify, and YouTube.
Some takeaways:
While average metrics are important, it’s crucial to also focus on edge cases and fail states. These rare but significant instances, like DoorDash’s “never delivered” orders, can have profound negative impacts despite their infrequency.
DoorDash converts metrics into a “common currency” to make better decisions, faster, about what to prioritize. They quantify business levers (e.g. price, selection, quality) in terms of their impact on a common metric like gross order value (GOV). For example, they know the relative impact on GOV for each of these changes:
Lowering price by a dollar
Lowering delivery times by a minute
Adding a new restaurant to the platform in a particular area
Once you understand how different metrics impact the “common currency,” you can understand the tradeoffs between different actions more accurately and prioritize more quickly for maximum impact.
Analytics is about driving business impact, not just providing a service to other functions when requested. Data teams should be involved in decision-making alongside engineering and product. They should not only surface insights about what is happening in the data but should have a point of view on what to do about it.
The best data analysts have soft skills, on top of table-stakes technical ability. Jessica loves analysts who are curious enough to dig deeper even when they’ve “answered the question.” She tests for this when hiring by including some flaws in her case studies to see if the candidates notice and/or how they respond when this is pointed out. She also loves analysts who can have a point of view with incomplete information and pivot with new information.
Where to find Jessica Lachs:
• LinkedIn: https://www.linkedin.com/in/jessica-lachs/
In this episode, we cover:
(00:00) Jessica’s background
(04:59) Centralized vs. embedded analytics teams
(10:52) The benefits of a centralized analytics team
(15:10) Balancing proactive and reactive work
(20:45) Advice on how to push back effectively
(24:20) Hiring for curiosity and problem solving
(28:57) Coming from a non-traditional background
(34:40) The early days and culture at DoorDash
(40:39) Encouraging cross-functional roles
(44:39) Defining effective metrics
(46:30) Simplifying metrics for better outcomes
(55:28) Focusing on edge cases and fail states
(01:00:12) Managing a global data organization
(01:02:31) Leveraging AI for productivity
(01:05:25) Building diverse and skilled data teams
(01:08:40) Lightning round
Referenced:
• How Netflix builds a culture of excellence | Elizabeth Stone (CTO): https://www.lennysnewsletter.com/p/how-netflix-builds-a-culture-of-excellence
• Riley Newman on LinkedIn: https://www.linkedin.com/in/rileynewman/
• Tony Xu on LinkedIn: https://www.linkedin.com/in/xutony/
• Imposter Syndrome: Why You May Feel Like a Fraud: https://www.verywellmind.com/imposter-syndrome-and-social-anxiety-disorder-4156469
• Stanley Tang on LinkedIn: https://www.linkedin.com/in/stanleytang/
• Andy Fang on LinkedIn: https://www.linkedin.com/in/fangsterr/
• Evan Moore on LinkedIn: https://www.linkedin.com/in/evanmoore/
• How WeDash became the flagship employee program for DoorDash: https://careers.doordash.com/blog/wedash-doordash-employee-program-how-does-it-work
• Leading with empathy | Keith Yandell (DoorDash, Uber): https://www.lennysnewsletter.com/p/leading-with-empathy-keith-yandell
• The Rose Code: https://www.amazon.com/Rose-Code-Novel-Kate-Quinn/dp/006305941X
• Libby app: https://libbyapp.com/
• The West Wing on Prime: https://www.amazon.com/West-Wing-Complete-First-Season/dp/B000KZPG04
• Alias on Prime: https://www.amazon.com/Alias-Season-1/dp/B00748O13S
• Joseon sunscreens: https://beautyofjoseon.com/
• Innisfree sunscreens: https://us.innisfree.com/
• John Steinbeck quote: https://www.brainyquote.com/quotes/john_steinbeck_103825
• Vanessa Roberts on LinkedIn: https://www.linkedin.com/in/vanessa-roberts-b8a509a/
• Tia Sherringham on LinkedIn: https://www.linkedin.com/in/tiasherringham/
• Elizabeth Jarvis-Shean on LinkedIn: https://www.linkedin.com/in/elizabeth-jarvis-shean-141a7966/
• Regina (Gina) Tarone on LinkedIn: https://www.linkedin.com/in/regina-tarone-a565a2/overlay/about-this-profile/
• My Journey (Part 1): I have a job that I would never be hired for: https://www.linkedin.com/posts/jessica-lachs_anniversary-datascience-finance-activity-7216912300056727553-mEv6/?utm_source=share&utm_medium=member_desktop
• Starting an Analytics Org From Scratch — Lessons From a Decade at DoorDash: https://review.firstround.com/starting-an-analytics-org-from-scratch-lessons-from-a-decade-at-doordash/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Lenny may be an investor in the companies discussed.
Building a world-class data org | Jessica Lachs (VP of Analytics and Data Science at DoorDash)