Product & Risk Analytics Manager

Rain is the world's first AI Financial Health Platform, serving 3.5 million employees at leading organizations like McDonald's, Marriott, and T-Mobile. Rain works in the background to optimize every employee's financial life to prevent shortfalls and build long-term stability. Backed by top investors including QED and Prosus, Rain has raised $150M in venture funding to fuel our next stage of hyper growth.About the Business Operations & Analytics Team

We are seeking a highly analytical and motivated Product & Risk Analytics Manager to join our Business Operations & Analytics team. This isn't just a reporting role; you will be the subject matter expert and storyteller of our data.

You will dive deep into our product, user, and transactional data, and repayment data to uncover critical insights that will shape our strategy, guide product development, and optimize our growth engine.

You will use your experience in risk, underwriting, and user behavior to develop strategies that minimize our risk and loss rates.

You will work cross-functionally with product, engineering, marketing, and leadership to answer our most pressing questions and turn complex data into actionable recommendations.

You will be reporting to the Head of Business Operations & Analytics with the option to work remotely or in-person in our downtown Nashville office.

What You’ll Do

• 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