Product & Risk Analytics Manager

Rain Technologies Inc.

23h•$130,000 - $150,000•Hybrid

About The Position

Rain is seeking a highly analytical and motivated Product & Risk Analytics Manager to join their Business Operations & Analytics team. This role involves being a subject matter expert and storyteller of data, diving deep into product, user, transactional, and repayment data to uncover critical insights that will shape strategy, guide product development, and optimize the growth engine. The manager will use their experience in risk, underwriting, and user behavior to develop strategies that minimize risk and loss rates. They will collaborate cross-functionally with product, engineering, marketing, and leadership to answer pressing questions and translate complex data into actionable recommendations. The position reports to the Head of Business Operations & Analytics and offers the option to work remotely or in-person in their downtown Nashville office.

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).
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.

Nice To Haves

Proven track record of building, mentoring, or scaling high-performing analytics teams.

Responsibilities

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.
Monitor production ML models for drift, track feature importance, and maintain data governance to ensure long-term risk strategy stability.
Build and scale Looker dashboards and LookML data models, managing code and SQL via Git/GitHub using engineering best practices (branching, PRs).
Perform deep dives into user behavior, acquisition funnels, and customer segmentation to drive product strategy, user engagement, and retention.
Design and analyze A/B tests, measure marketing campaign effectiveness, model CAC and LTV, and track company-wide KPIs.
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.