Staff Data Scientist

Midi Health; Palo Alto (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Midi Health in Palo Alto is seeking a highly strategic Senior or Staff Data Scientist to design and own the data framework that defines business health. You'll connect multi-product lifecycles and financial metrics influencing key company decisions.

The ideal candidate has 8+ years of experience and a Master’s or PhD in a quantitative discipline, with strong skills in advanced modeling, Python, and SQL. This hybrid role requires a strategic mindset and collaboration with executive leadership.

Qualifications

• 8+ years of experience delivering high-impact data science solutions.
• Mastery of predictive modeling and causal inference techniques.
• Proven experience building production-grade machine learning models.

Responsibilities

• Develop sophisticated lifetime value models across healthcare reimbursements.
• Build predictive models for reimbursement rates and margin constraints.
• Advise leadership on business stress tests via macro-simulations.

Skills

Advanced Modeling & Stats

Production-Grade Engineering

Expert-Level Evaluation

Attribution & LTV

Programming & Querying

Simulation Design

Education

Master’s or PhD in Economics, Econometrics, Applied Statistics

Tools

Python

SQL

Job description

Hybrid, Palo Alto (Hybrid – 2 days/week in office). Reports to: Director Data Science + Analytics.

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.
• 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."

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 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.

Analytical Capabilities

• 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.

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.

Equal Opportunity Employer

Midi Health is an Equal Opportunity Employer. We are committed to pay equity and ensure that all qualified applicants receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. Our compensation philosophy is based on fair, objective criteria and the impact of the role, regardless of an applicant’s salary history.