Product & Risk Analytics Manager

Rain is an AI financial health platform serving employees at leading organizations. The Product & Risk Analytics Manager will analyze product, user, transactional, and repayment data to develop risk models, guide product and growth strategy, and reduce losses. The role partners with product, engineering, marketing, finance, and leadership to translate complex data into actionable recommendations.

Responsibilities:

• Risk & ML Modeling: Analyze product, user, and repayment data to design, deploy, and refine machine learning risk/underwriting models—focusing on loss mitigation, ACH failure reduction, and fraud prevention
• Model Lifecycle & Performance Monitoring: Monitor production ML models for drift, track feature importance, and maintain data governance to ensure long-term risk strategy stability
• BI Infrastructure & Version Control: Build and scale Looker dashboards and LookML data models, managing code and SQL via Git/GitHub using engineering best practices (branching, PRs)
• Product & User Analytics: Perform deep dives into user behavior, acquisition funnels, and customer segmentation to drive product strategy, user engagement, and retention
• Experimentation & Growth: Design and analyze A/B tests, measure marketing campaign effectiveness, model CAC and LTV, and track company-wide KPIs
• Cross-Functional Leadership: Partner with Engineering, Product, and Finance to ensure data accuracy, translate complex insights into executive narratives, and elevate the organization's overall data fluency
• Improve the overall analytical quotient of the team and organization as a whole•

Requirements:

• 5–7 years in data, product, and risk analytics, with proven success building and deploying credit/banking risk models
• Expert SQL (Snowflake/complex queries), proficiency in Python ML libraries (Pandas, Scipy, XGBoost), and hands-on experience with BI tools (Looker, Superset)
• Deep understanding of statistical principles, hypothesis testing, and hands-on A/B experimentation
• Thrives as an individual contributor in fast-paced, ambiguous startup environments, leveraging AI tools to scale output quality and efficiency
• Capable of delivering immediate IC impact while scaling with the business to build and lead a team
• Proven track record of building, mentoring, or scaling high-performing analytics teams