Senior Product Data Analyst
New
C
CookUnityFood marketplace
Location: Latam (Remote)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Approximately 5–7 years in product analytics or a closely related analytical role
- Required Skills
- SQLProduct AnalyticsA/B testing
Requirements
- Have approximately 5–7 years of experience in product analytics or a closely related analytical role, with ownership of complex analyses and influence on product decisions; equivalent experience and evidence of impact are welcome.
- Demonstrate strong SQL skills and hands-on experience analyzing large, complex behavioral datasets.
- Be able to validate data and write clear, reusable analyses.
- Have practical command of product metrics, funnel and cohort analysis, segmentation, and marketplace or subscription dynamics.
- Have experience designing and interpreting A/B tests, including holdouts, guardrails, uncertainty, and tradeoffs.
- Use sound judgment about causal claims and explain the limits of evidence.
- Be able to turn an ambiguous product problem into a focused learning plan and actionable recommendation.
- Build intuitive dashboards that answer product questions, reveal insights, and support trustworthy self-service analysis.
- Use AI tools practically for analysis, coding, research synthesis, documentation, and repeatable workflows; verify outputs and handle company and customer data responsibly.
- Evaluate AI-enabled product experiences through success metrics, experiments, user feedback, and appropriate quality and safety checks.
- Partner constructively with Product, Design, Engineering, User Research, and business teams, and communicate data insights clearly.
Responsibilities
- Analyze how customers browse chefs, meals, cuisines, dietary options, and price points, including the effects of assortment, variety, and personalization on ordering behavior.
- Map the customer journey from acquisition through first order, repeat orders, and longer-term retention, and identify friction across onboarding, menu exploration, checkout, and weekly ordering.
- Define hypotheses, primary metrics, guardrails, sample sizes, and holdouts for product experiments; analyze results and recommend whether to launch, iterate, or stop.
- Use observational approaches when randomized tests are impractical, and explain the limits of the evidence.
- Evaluate product changes against order rate, churn, revenue per customer, discounts, chef engagement, and unit economics.
- Combine behavioral data with research and customer feedback to identify friction in subscriptions, delivery information, account management, and support journeys.
- Define and document product metrics, cohorts, event tracking, and dashboards, and work with Engineering and Data partners to validate instrumentation.
- Build dashboards that help product teams explore results, identify patterns, and monitor outcomes after launch.
- Partner with Product, Design, and User Research to prioritize unknowns, design analyses and tests, and communicate what teams learned.
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