Data Scientist

Requirements

• 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 Python for complex statistical analysis, alongside expert-level SQL for manipulating large data streams
• Simulation Design:
  Experience structuring systemic business simulations or stochastic modeling
• Modern AI Workflow:
  Active adoption and mastery of Large Language Models (LLMs) and generative AI tools within your personal development workflow to accelerate coding, debugging, documentation, and prototyping
• Unit Economics Intuition:
  You have a deep, near-obsessive understanding of the relationship between CAC, LTV, payback periods, gross margins, and contribution margins
• Business Acumen:
  The ability to translate complex statistical outputs into clean, actionable frameworks for the CFO, CMO, and executive leaders. You know how to influence cross-functional roadmaps with data
• Strategic Problem Structuring:
  Ability to take vague, complex business questions and break them down into answerable, high-impact analytical components
• 8+ years of experience delivering high-impact data science solutions
• Master’s or PhD in Economics, Econometrics, Applied Statistics, or a related quantitative discipline
• Ideally, your background includes time in Marketplaces, Healthcare operations, or D2C subscription businesses
• Demonstrated progression in scope and impact, with a history of acting as a strategic partner to finance and operations teams

• Reports to:
  
  Director Data Science + Analytics
• 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 life cycles, complex healthcare reimbursement cycles, and operational cost structures
• Our 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
• 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…