DoorDash has been the professional adventure of a lifetime for me. I joined as the second data scientist at the company. Not a lot of people focused on machine learning at that time, since we were very operationally driven and already had a maniacal focus on execution. Perhaps my most transformative experience was leading the development of a new dasher pay algorithm in 2019. Our task was to build a machine learning system that calculated pay for each order in a way that compensated dashers fairly for their effort.
Four and a half years later since I’ve joined, I’ve grown tremendously and DoorDash has truly evolved as a company. We have machine learning models across all of our core businesses and are rapidly expanding into new verticals like convenience and grocery. I’ve had opportunities to work on impactful projects while deepening my technical expertise. I’m very grateful for all the people I’ve met here, and the problems I’ve been able to work on.
#2 – How did you get into data science?
I’ve always been interested in understanding human decision-making. In college, I studied economics and dedicated my senior thesis to analyzing the nonrational yet realistic side of economic behavior. In my first job as a strategy consultant, I saw how powerful data was in quantifying and explaining human behaviors like brand-loyalty driven purchasing. I felt like data-driven analytical tools provided an incredible avenue to understand real-life economic behaviors. I went to a bootcamp for data science and haven’t looked back. I’m so grateful for the opportunity to have learned on the job here at Doordash.
#3 – You’re speaking at the upcoming Rework Deep Learning Summit – what is your talk about?
I’m presenting on the evolution of our substitutions recommendations algorithm, which has been my primary focus for the past year. Since DoorDash started delivering from convenience and grocery stores, a key consumer pain point we’ve tried to solve is item availability and substitutions. When a customer orders an item, there’s a chance that item is out of stock at the store (this has been exacerbated by recent supply chain issues), so it’s important for us to provide good recommendations for appropriate substitutes.
When I first started working on this problem we had relatively little data but after working closely with the product and operations team, we released several features that enabled us to collect customer substitution preferences. This data allowed us to use more state-of-the-art algorithms and improve the quality of our recommendations.
#4 – What has your experience in our WeDash program been?
WeDashing is a very nostalgic experience for me. When I first joined DoorDash back in 2017, we used to go on WeDashes as a team and it was always a great bonding experience. We got to interact with merchants, customers and other dashers, who had a lot to teach us about how to improve our product.
#5 – What Learning & Development (L&D) programs have you participated in (if any) and how have they supported you at DoorDash?
I recently participated in the Elevate program here at DoorDash, which is an incredible program to empower women of color through professional coaching, access to leadership, and a nurturing community. I’ve been lucky to have very supportive managers and mentors here. All of these have helped me to find a more confident voice and grow in my career.
#6 – What challenges have you overcome as a woman in tech?
To be honest, this is still a work in progress for me. Like many women and underrepresented minorities, I struggle with impostor syndrome. Despite the experience and expertise I’ve developed, I’m still aware of the limitations in my technical skill-set and soft-skills. Sometimes this leads me to not speak up in decision-making meetings and instead support initiatives and ideas led by others. This is why I think programs like Elevate are so important for companies to adopt.
#7 – What do you love most about working at DoorDash, and what do you think is important for potential candidates to know?
What I love most about DoorDash is our culture of first-principles thinking and our deep obsession about the customer from the IC to the executive level.
To give an example, our President Christopher Payne sent a message the other day about substitution recommendations in his grocery order (this is the area I’m working on right now). I took a step back to appreciate that Christopher orders groceries through our platform regularly and took the effort to write such a thorough message with detailed screenshots about his experience as a customer despite the 20,000 things he had to do that day. I see that level of obsession across the organization, and it makes me proud to be here.
Check out open roles on the Data Science team and apply to an open role today!