About this role
EPCVIP is hiring a Product Analyst to run the day-to-day execution of funnel and acquisition experiments within Product Operations. At EPCVIP, w e run a high-volume consumer-lending marketplace that connects people with financial offers through a network of partners. We continuously experiment to improve conversion, lead quality, and marketplace performance. In this role you will own assigned initiatives from hypothesis and implementation through analysis and recommendation. This is an execution and analysis role for someone early in their product career who already works comfortably with AI tools. You will use Claude Code, our internal skill library, and data warehouse integrations to develop and verify queries, structure analyses, and produce clear readouts. The tools accelerate the work, but you remain responsible for validating the logic, data, and conclusions. Responsibilities: Pre-Experiment Analysis and Sizing Define the hypothesis, success metrics, guardrails, and decision criteria for assigned initiatives Estimate expected lift, required sample size, statistical power, and test duration before committing to a build Use historical and segment-level data to evaluate opportunities and support prioritization Contribute ideas based on user behavior, competitor funnels, mock-ups, and performance trends Experiment Execution Run assigned lead-acquisition experiments from approved concept through final decision Coordinate implementation with Design and Engineering, complete QA, launch the test, and monitor live performance Analyze results, evaluate statistical significance, and document a clear recommendation Maintain experiment tickets, documentation, timelines, and status visibility Surface tracking gaps, sample-ratio mismatch, unexpected metric movement, and other anomalies early Product and Funnel Analysis Investigate funnel performance, conversion trends, revenue movements, and stakeholder questions Frame analytical questions, segment the data, and conduct analyses using SQL, Python, and AI tooling Validate query logic, data quality, and unexpected findings before reaching a conclusion Produce concise, shareable readouts with clear findings, limitations, recommendations, and next steps Partner with BI when work requires deeper data modeling or more extensive analysis Cross-Functional Partnership Keep assigned initiatives moving across Product, Engineering, Design, QA, and BI Work with your manager on prioritization, interpretation, and key decisions Translate findings into clear recommendations for technical and non-technical audiences Escalate blockers, risks, and uncertainty early