About the Team

The north star of New Verticals Business Operations is to explain what happened, why, and what do we do about it for our key business metrics. Over the past 2+ years, we have partnered with New Verticals operations, data science, product, marketing, and more to quantify the inputs that move metrics, tell the story of business performance on a week to week basis, and surface net new opportunities for growth.

About the Role

As a Data Scientist on the Retail BizOps team, you'll support DoorDash's retail expansion by analyzing customer journeys across key categories like Household, Electronics, and Apparel. You'll decode retail trends, develop data-backed strategies to accelerate adoption, and present insights directly to leadership that influence both tactical decisions and long-term strategy.

Your work will involve deep dives into category performance and customer behavior, building sophisticated diagnostic tools, and creating dashboards that identify growth opportunities. This high-visibility role requires extracting meaningful stories from complex data to help transform DoorDash into the most convenient shopping destination while advancing the company's ambitious retail vision.

You're excited about this opportunity because you will… Decompose topline metric movements into internal (experiments, regulatory changes) vs. external (macro, weather, holidays, etc.) changes and surface key drivers to XFN stakeholders. Provide a forward-looking outlook of what to expect in the coming weeks based on ramping experiments, occasions, prior year trends, etc. Deepen our understanding of key metrics via deep dives and ad-hoc analysis (ie, surfacing a new trend that impacts weekly topline movement). Specific deliverables are typically in the form of dashboards that decompose w/w, m/m, etc., movement in certain metrics, regular data storytelling of metric movements We're excited about you because you have… A degree in Math, Physics, Statistics, Economics, Computer Science, or a similar domain Experience working with funnel optimization, user segmentation, cohort analyses, time series analyses, regression models, etc Expertise of SQL queries, ETL, A/B Testing, and statistical analysis (e.g. hypothesis testing, experimentation, regressions) with statistical packages, such as Matlab, R, SAS or Python Proficiency in one or more analytics & visualization tools (e.g. Chartio, Looker, Tableau) The insight to take ambiguous problems and solve them in a structured, hypothesis-driven, data-supported way

 

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