Staff Data Scientist

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 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.

Experience

• 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.

Interview Process

Recruiter Screen- 30 mins Hiring Manager Screen- 45 mins Technical Screen- 1hr Panel Interviews- 2-3 hours + Lunch in Office in Palo Alto

At this time, Midi is unable to provide visa sponsorship. Candidates must be authorized to work in the U.S. without current or future sponsorship needs.

The Salary range base salary is 210-250K and will depend on experience.Midi pays a competitive base salary, plus equity and benefits.

While you’re waiting for us to review your portfolio, here’s some fun content to check out 🎥 https://www.youtube.com/watch?v=1px7i6MVjNg

At this time, Midi is unable to provide visa sponsorship. All Candidates must be authorized to work in the United States without current or future sponsorship needs.

Please note that all official communication from Midi Health will come from an @joinmidi.com email address. We will never ask for payment of any kind during the application or hiring process. If you receive any suspicious communication claiming to be from Midi Health, please report it immediately by emailing us at careers@joinmidi.com.

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• 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.

• 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.

• 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."

• 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?"

• 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.