Responsibilities

• Lead the end-to-end analytics program across product, marketing, and rewards, setting the measurement strategy and operating model for the company.
• Build and maintain source-of-truth dashboards, core metrics, and instrumentation standards in partnership with Data Engineering.
• Run robust experimentation at scale—A/B tests, geo/holdouts, matched markets—and drive causal measurement using CUPED, uplift modeling, and other advanced methods.
• Own funnel, cohort, retention, and rewards analytics (cashback, Adventures/Gigs, action incentives); identify leaks, quantify opportunities, and propose optimized reward structures.
• Stand up omnichannel marketing measurement across lifecycle, partners/affiliates, paid social/search, and influencers; lead incrementality and attribution frameworks.
• Develop LTV, payback, forecasting, and rewards economics models to guide roadmap, budget allocation, portfolio mix, and CAC/LTV guardrails.
• Publish weekly and monthly executive readouts with decision-ready insights; influence prioritization, staffing, and budget decisions.
• Ensure data quality and integrity through tracking requirements, validation, anomaly detection, and fraud/abuse monitoring (velocity, collusion, partner attribution, CPA integrity).
• Build self-serve semantic layers and Looker dashboards that enable teams to independently answer key questions.

Requirements

• BS/BA in a quantitative field (Statistics, Economics, Computer Science, Engineering, Data Science) or equivalent; MS a plus.
• 3+ years in product, growth, or marketing analytics, incentives/loyalty analytics, or data science, with 1+ years in a lead or ownership role.
• Advanced SQL and Python (pandas, statsmodels, causal inference); experience building scalable dashboards and pipelines in BigQuery and Looker.
• Expertise in experimentation design: A/B tests, CUPED, geo/holdouts, power analysis, uplift modeling; familiarity with experimentation platforms like Eppo, Statsig, or LaunchDarkly.
• Strong analytics and modeling skills: LTV, propensity, churn/survival, MMM, budget/payout optimization, and causal inference methods.
• Product and marketing analytics experience: funnels, retention, cohorting, lifecycle measurement, attribution, channel mix/ROAS, and audience targeting.
• Strong product sense with the ability to translate complex data insights into clear, actionable recommendations that drive product and growth decisions.

Preferred

• Comfortable operating at both altitude (strategy, exec forums) and depth (hands‑on SQL/Python).
• Excellent written storytelling; exec‑ready visuals and clear recommendations.
• Ability to manage multiple high‑stakes initiatives simultaneously; thrive in a fast‑paced startup.
• Collaborate effectively across time zones.