Senior Product Data Analyst

About CookUnity:

Food has lost its soul to modern convenience. And with it, it has lost the power to nourish, inspire, and connect us. So in 2018, CookUnity was founded as the first-of-its-kind platform that connects the world with the source of truly great food: chefs. Today, CookUnity delivers 50 million meals a year from the industry’s best chefs to homes all over the country. Fresh. Ready-to-eat. And crafted with the passion that nourishes body and soul.

Unwilling to stop there, CookUnity is expanding beyond delivery to become an ever-innovating marketplace focused on our singular mission:

empower Chefs to nourish the world.

If that mission has you hungry in more ways than one, you’ve found the right job posting.

About the Role

As a Senior Product Data Analyst

, you will be an analytical partner to Product, Design, Engineering, and User Research. You will help teams understand both sides of CookUnity's marketplace: how customers find meals that fit their lives and how chef supply, variety, and merchandising shape that experience. You will own measurement and analysis for a product area, turn ambiguous questions into testable hypotheses, and use evidence to guide product decisions.

Our teams operate through clear success metrics, rapid learning, and controlled experiments. You will help them distinguish meaningful customer and business outcomes from activity, evaluate tradeoffs across the funnel and customer lifecycle, and make insights accessible in day-to-day decisions. This is a hands-on senior individual contributor role with meaningful autonomy and close cross-functional partnership.

What You'll Do in 12 Months

• 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 without hiding weak experiences behind averages.
• 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 scale.
• 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 to another channel.
• 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.
• Shape a focused learning agenda: Work with Product, Design, and User Research to prioritize the most consequential unknowns, design analyses and tests, and tell a clear story about what the team learned. Share methods and review work with other analysts to raise the quality of product analytics across the team.

What Success Looks Like

Product teams use clear metrics and guardrails to make decisions about discovery, conversion, ordering, and retention.

Major launches are supported by credible tests or well-explained evidence, including their impact on different customer segments.

Insights lead to measurable improvements in customer experience and repeat ordering while accounting for marketplace and financial tradeoffs.

Stakeholders trust and regularly use self-service reporting, and core behavioral data is sufficiently complete to diagnose product performance.

Core Competences/Requirements

Approximately 5–7 years in product analytics or a closely related analytical role, with demonstrated ownership of complex analyses and influence on product decisions. Equivalent experience and evidence of impact are welcome.

Practical fluency with AI tools for accelerating analysis, coding, research synthesis, documentation, and repeatable workflows; able to verify outputs and handle company and customer data responsibly.

Strong SQL and hands-on experience analyzing large, complex behavioral datasets; ability to validate data and write clear, reusable analyses.

Practical command of product metrics, funnel and cohort analysis, segmentation, and marketplace or subscription dynamics.

Experience designing and interpreting A/B tests, including holdouts, guardrails, uncertainty, and tradeoffs; sound judgment about causal claims.

Ability to turn an ambiguous product problem into a focused learning plan, work independently, and communicate an actionable recommendation.

Clear data storytelling and constructive partnership with Product, Design, Engineering, User Research, and business teams.

Ability to build powerful, intuitive dashboards in any tool that answer important product questions, reveal actionable insights, and enable trustworthy self-service analysis.

Ability to evaluate AI-enabled product experiences through clear success metrics, experiments, user feedback, and appropriate quality and safety checks.

Curiosity about food, chefs, customer behavior, and the practical constraints of operating a local marketplace.