Job Description
📋 Description Be analytics partner inside the pod; define questions, metrics, guardrails, rules. Run rigorous experiments with A/B tests and pre-defined success criteria. Connect behavior to strategy using funnel, cohort, and lifecycle analysis. Apply causal inference when experiments aren't possible (diff-in-diff, matching, regression). Build predictive models with feature engineering, validation, and monitoring. Create decision-ready analyses and narratives to drive action. 🎯 Requirements 6+ years using data to drive product/business decisions. Strong SQL and Python or R for analysis and modeling. Deep experimentation and causal inference; Bayesian exp preferred. Practical modeling: feature engineering, validation, backtesting, calibration. Strong product sense; connect analytics to measurable outcomes. Clear communication: explain complex work to non-technical audiences. 🎁 Benefits Equity Compensation Medical, Dental, and Vision coverage HSA / FSA 401K Work-from-Home Stipend Therapy Reimbursement
