Data Scientist, Clinical Outcomes

Midi Health; San Francisco (CA); Hybrid; USD 144,000 - 172,000; Full time

Job summary

Midi Health in the San Francisco Bay Area seeks a pragmatic Data Scientist to own the quantitative foundation for clinical care delivery, patient treatment plan refinement, and retention engines. You will bridge structured clinical metrics with unstructured patient communications to build decision-support tools and personalized care pathways.

You’ll transform messy, multi-modal data into reliable features, develop NLP pipelines for transcripts, and collaborate with clinical operations to drive

Qualifications

• 2 to 4 years of hands-on data science or analytics experience delivering high-impact solutions.
• Experience with healthcare, telehealth, digital health datasets.
• Experience parsing unstructured text/transcripts into structured modeling pipelines.

Responsibilities

• Clean and transform multi-modal clinical data into reliable modeling tables and feature stores.
• Establish unstructured data workflows to convert notes and transcripts into structured tables.
• Extract patient intent and preferences from transcripts using lightweight NLP/LLM pipelines.
• Identify friction points and potential retention risks from qualitative dialogue.
• Develop lookalike cohorts and forecast outcomes for treatment planning.
• Collaborate with Clinical Ops and Product to translate analytics into actionable workflows.

Skills

SQL

Python

NLP

LLM workflows

Machine Learning

Causal inference

Propensity score matching

Survival analysis

Data visualization

Communication

Education

Bachelor’s or Master’s degree in Biomedical Data Science/Health Informatics/Biostatistics or related quantitative field

Tools

Transformers

Claude API

Job description

Hybrid, Palo Alto or San Francisco (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 pragmatic, hands-on Data Scientist to build and own the quantitative foundation for our clinical care delivery, patient treatment plan refinement, and retention engines.

In our specialty care model, clinical health outcomes and business growth are directly linked: matching patients to their preferred, personalized treatment path leads to higher clinical efficacy, better satisfaction, and long‑term retention. In this role, you won't just work with clean tabular data; you will bridge the gap between structured clinical metrics (lab values, symptom severity scores, dosage trajectories) and unstructured patient communications (visit transcripts, ambient scribe outputs, and intake survey dialogue).

You will sit at the intersection of Data Science, Clinical Operations, and Product, serving as a core builder who transforms messy, multi‑modal patient data into actionable clinical decision support and personalized care pathways.

What You’ll Do

• Clean & Transform Multi-Modal Clinical Data: Build reliable, well-structured modeling tables and feature stores by parsing, cleaning, and joining raw datasets including lab results, symptom severity surveys, medication history, and raw visit transcripts.
• Establish Unstructured Data Workflows: Design robust data processing pipelines to convert unstructured clinical notes, orders and visit transcripts into structured, queryable relational tables.

Transcript Mining & Feature Extraction

• Extract Patient Intent & Preference: Build lightweight NLP and LLM‑based pipelines (using entity extraction, sentiment analysis, and topic modeling) to convert ambient visit transcripts into structured features. And provide insights into patient preferences in treatment pathway, communication, and modality, etc.
• Identify Friction Points: Process qualitative dialogue to automatically detect unaddressed obstacles or early side‑effect reports before they lead to treatment discontinuation.

Lookalike Cohorts & Patient Treatment Planning

• Lookalike Clustering Engine: Develop unsupervised segmentation models to cluster patients into "lookalike" cohorts based on baseline lab values, symptom profiles, and transcript‑derived preferences.
• Outcome & Trajectory Forecasting: Build predictive models that estimate symptom reduction, time‑to‑relief, and potential side‑effect risks across alternative treatment branches (e.g., immediate HRT vs. conservative step‑therapy).

Causal Inference & Retention Analytics

• Quasi-Experimentation on Care Protocols: Apply observational study methods (Propensity Score Matching, Difference-in-Differences, Survival Analysis) to evaluate non-randomized care pathways and quantify counterfactual patient outcomes.
• Drop-Off & Retention Optimization: Identify critical clinical touchpoints and dosage adjustment milestones that drive long‑term program adherence, connecting clinical milestone achievement directly to patient retention and LTV.

Research & Cross-Functional Collaboration

• Evidence Generation: Partner with the clinical research team to provide analytical support for peer‑reviewed medical publications, white papers, and payer studies.
• Clinical Operations Alignment: Translate analytical findings, treatment protocol insights, and patient drop‑off trends into actionable workflow improvements for clinical operations and care teams.

Technical Skills

• Programming & Querying: Advanced proficiency in SQL for data manipulation alongside strong Python skills for predictive modeling and data transformation.
• Machine Learning & Modeling: Practical experience applying clustering (segmentation), regression/classification, and survival analysis (e.g., scikit-learn, statsmodels, lifelines).
• NLP & Modern LLM Workflows: Hands‑on experience with NLP techniques and LLM frameworks (e.g., transformers, Claude API, etc.) to parse, extract, and classify unstructured text data.
• Causal Inference Fundamentals: Solid working knowledge of quasi-experimental design, propensity score matching, and counterfactual reasoning on observational datasets.

Analytical Capabilities

• Healthcare Data Intuition: Comfort working with messy, semi-structured, or missing healthcare datasets (lab results, Patient-Reported Outcome Measures/PROMs, EHR notes).
• Pragmatic "0-to-1" Builder Mindset: You enjoy doing the foundational data architecture work—cleaning JSON outputs, structuring features, and validating baseline models—before reaching for complex algorithms.
• Cross-Functional Communication: Ability to translate technical modeling outcomes into clear, intuitive insights for Clinical Operations and Product partners.

Experience

• 2 to 4 years of hands‑on data science or analytics experience delivering high‑impact solutions.
• Experience working with healthcare, telehealth, digital health datasets.
• Demonstrated track record of parsing unstructured text/transcript data into structured modeling pipelines.
• Bachelor’s or Master’s degree in Biomedical Data Science, Health Informatics, Biostatistics, or a related quantitative field.

Interview Process:

Recruiter Screen- 30 mins
Hiring Manager Screen- 30 mins
Technical Screen- 1hr
Panel Interviews- 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.

This is a full‑time W2 position. The base salary range is 144K-172K and will depend on experience. Midi pays a competitive base salary, plus equity and benefits.

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.comemail address. We willnever 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 [e-mail] .

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.

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