Senior Product Data Analyst

Responsibilities

• Make the marketplace easier to discover and more relevant: Study how customers browse chefs, meals, cuisines, dietary options, and price points.
• Analyze the relationship between local assortment, variety, personalization, and ordering behavior. Help Product identify where better discovery or merchandising can improve repeat ordering and retention.
• Improve the journey from first visit to lasting habit: Map the customer funnel from acquisition through first order, early repeat orders, and longer-term retention. Find friction in onboarding, menu exploration, checkout, and weekly ordering. Segment customers thoughtfully so teams can improve conversion and retention…
• Measure product bets with credible experiments
• Partner with Product and Engineering to define hypotheses, primary metrics, guardrails, sample sizes, and holdouts for changes to the funnel, personalization, loyalty, and in-product interventions.
• Analyze heterogeneous effects and uncertainty, and recommend whether to launch, iterate, or stop. When a randomized test is impractical, use the strongest available observational approach and explain its limits.
• Connect customer value to marketplace economics: Help teams evaluate how product changes affect order rate, churn, revenue per customer, discounts, chef engagement, and unit economics together. For new categories and ordering formats, define the measures that show whether customers find value and whether the model can…
• Reduce the work and stress of ordering: Combine behavioral data with research and customer feedback to understand where subscriptions, delivery information, account management, or support journeys create friction. Measure whether self-service and product improvements solve the underlying problem, not just move contacts…
• Build a trusted measurement foundation: Define and document product metrics, cohorts, event tracking, and dashboards. Work with Engineering and Data partners to validate instrumentation and maintain reliable behavioral data.
• Create compelling dashboards that let product teams explore results, spot meaningful patterns, and monitor outcomes after launch.

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