Staff Data Scientist

📍 Hybrid, Palo Alto (Hybrid – 2 days/week in office) Reports to: Director Data Science + Analytics Job Type: Full-time, W2 About the Role We are looking for a highly strategic Senior or Staff Data Scientist to design, build, and own the end-to-end data framework that defines our business health: Unit Economics. In this role, you won't just build standalone models; you will connect the dots between customer acquisition, multi-product lifecycles, complex healthcare reimbursement cycles, and operational cost structures. Your work will serve as the financial and analytical source of truth, directly influencing how we allocate marketing spend, price our products, manage retention, and project long-term profitability. You will sit at the intersection of Data Science, Finance, Marketing, and Operations, acting as a critical strategic partner to executive leadership. What You’ll Do Unified LTV & Reimbursement Modeling Bridge Estimated vs. Realized LTV: Develop sophisticated lifetime value models that account for the volatility of healthcare reimbursements and the time value of money. Predictive Reimbursement Rates: Build models to predict actual reimbursement rates across a complex mix of insurance allowables and self-pay tracks, closing the gap between theoretical revenue and cash-in-hand. Integrate Margin Constraints: Establish the foundational frameworks that incorporate operational realities—such as state-by-state clinician licensing costs and wage ranges—ensuring our LTV calculations reflect true contribution margins. Cross-Product Attribution & Portfolio Optimization Blended Contribution Margin: Optimize "basket composition" and cross-sell dynamics between our physical supplement lines and clinical services to maximize total margin. Multi-Touch & Cross-Product Attribution: Build advanced attribution models (Markov chain, ML-based) to quantify the interplay between product lines—specifically tracking how supplement purchases drive clinical visit adoption and vice versa. Price Elasticity: Design and analyze pricing experiments for supplement products to identify optimal margin-maximizing price points without degrading long-term subscriber retention. Causal Inference & Growth Intelligence Influence the CAC Decision Curve: Utilize your LTV and margin frameworks to influence the marginal LTV curves that marketing uses, helping them determine the exact point of diminishing returns on ad spend. Causal Churn Intervention: Move beyond simple churn prediction. Build uplift models to identify which at-risk customers will respond positively to specific interventions (e.g., targeted offers, clinical outreach), preserving margin by avoiding unnecessary discounting on "sure things" or "lost causes." Strategic Macro-Simulation Systemic Stress-Testing: Build stochastic (Monte Carlo) macro-simulations to help leadership and finance stress-test our business model. You will answer questions like: "If a major insurance payer shifts an allowable rate in a key state, how does that impact our payback period and portfolio margin?" What You Bring Technical Skills Advanced Modeling & Stats: Mastery of predictive modeling and Causal Inference techniques (e.g., uplift modeling, propensity score matching, synthetic controls, or diff-in-diff). Production-Grade Engineering: Proven experience architecture - building, deploying, and maintaining production-grade machine learning models. You write clean, modular, and well-tested code that integrates seamlessly into downstream workflows. Expert-Level Evaluation: Deep expertise in model evaluation methodologies, backtesting, and validation. Because your models directly impact financial forecasts and pricing decisions, you have a rigorous approach to error analysis, cross-validation, and drift detection. Attribution & LTV: Proven track record building attribution models (algorithmic or heuristic) and handling survival analysis for churn and retention forecasting. Programming & Querying: Advanced proficiency in Pyth…