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About This Role
Hybrid, *Palo Alto (Hybrid – 2 days/week in office)*
*Reports to: Director Data Science \+ Analytics*
About the Role
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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
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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
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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.
#### 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\-240K 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
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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 [email protected].
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.
Please find our CCPA Privacy Notice for California Candidates here.
Salary Context
This $210K-$240K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Midi Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($225K) sits 14% above the category median. Disclosed range: $210K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Midi Health AI Hiring
Midi Health has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US. Compensation range: $240K - $240K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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