Responsibilities

• Be the analytical thought partner for lifecycle, CRM, and retention strategy.
• Partner closely with PMM and Product to identify the biggest opportunities to improve retention, re-engagement, and long-term user value. Use cohort analysis and lifecycle insights to help prioritize where the business should invest.
• Own measurement strategy for lifecycle programs and experiments.
• Define success metrics and measurement frameworks for CRM and lifecycle initiatives. Design, execute, and evaluate A/B tests and other experiments to understand what drives retention, engagement, and sustained behavior change.
• Drive cohort-based insights that improve user retention.
• Analyze user behavior across lifecycle stages to identify patterns, drop-off points, and high-impact levers for retention. Go beyond reporting to uncover actionable insights that inform targeting, messaging, timing, and program design.
• Support operational excellence in experimentation and reporting.
• Be self-sufficient in setting up analyses, monitoring experiment performance, identifying issues, and delivering clear recommendations. Build scalable dashboards, reporting cadences, and frameworks that enable fast, high-quality decision-making.
• Communicate insights clearly and influence through data.
• Translate complex analysis into concise, compelling narratives for technical and non-technical audiences. Present findings and recommendations in a way that drives alignment across PMM, Product, and leadership stakeholders.

Requirements

• Strong lifecycle / CRM / retention analytics experience.
• 8+ years of experience in analytics in a fast-paced, data-driven environment, ideally in consumer tech. Strong background in lifecycle marketing, CRM analytics, retention, engagement, cohort analysis, and user behavior measurement.
• Deep experimentation and measurement capability.
• Proven experience designing, executing, and interpreting A/B tests and other growth experiments. Able to independently define metrics, assess results, identify tradeoffs, and make clear business recommendations.
• Expert SQL skills.
• Highly proficient in SQL and comfortable using data independently to answer business questions and support experimentation. Python is helpful but not required.
• Operational and self-sufficient.
• Comfortable owning analyses end-to-end, working in self-serve environments, and moving from problem definition through implementation support, measurement, and recommendation.
• Excellent communication and presentation skills.
• Able to synthesize complex findings into clear takeaways for a wide range of audiences. Comfortable influencing cross-functional teams and presenting insights and recommendations to senior stakeholders.