Lead Product Analyst, Lifecycle Marketing

Pinterest•New York, NY

8d•$145,747 - $300,067•Remote

About The Position

As the Lead Product Analyst- Lifecycle Marketing, you will serve as the strategic analytics partner for lifecycle and CRM initiatives, helping shape retention strategy through cohort-based insights, experimentation, and cross-functional partnership. You will partner deeply with PMM, Product, and Data Science to measure impact, improve lifecycle programs, and identify the highest-leverage opportunities to drive retention. This role is ideal for someone who combines strong analytical rigor, operational execution, and excellent communication skills. You should be comfortable moving from hands-on analysis to clear recommendations, and from detailed experiment readouts to polished presentations for cross-functional and leadership audiences.

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.
Cross-functional partnership strength.
Proven ability to work closely with PMM, Product, and Engineering. Collaborative, low ego, and effective at aligning partners around data-informed decisions.
Retention-oriented mindset.
Understands how to evaluate user behavior over time, identify retention drivers, and distinguish short-term engagement lifts from sustained long-term impact.
AI-augmented analytical workflow.
Demonstrated ability to use AI tools to improve analytical efficiency, synthesis, or workflow speed, while maintaining sound judgment, validating outputs, and owning final recommendations.
Graduate degree in a quantitative field such as mathematics, statistics, computer science, engineering, or equivalent practical experience.

Nice To Haves

Python is helpful but not required.

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.