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About the Role
We are seeking 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. This role sits at the intersection of Data Science, Clinical Operations, and Product, transforming multi-modal patient data into actionable clinical decision support and personalized care pathways.
Responsibilities
- 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.
- Design robust data processing pipelines to convert unstructured clinical notes, orders and visit transcripts into structured, queryable relational tables.
- Build lightweight NLP and LLM-based pipelines to convert ambient visit transcripts into structured features, providing insights into patient preferences.
- Process qualitative dialogue to automatically detect unaddressed obstacles or early side-effect reports.
- Develop unsupervised segmentation models to cluster patients into "lookalike" cohorts.
- Build predictive models that estimate symptom reduction, time-to-relief, and potential side-effect risks.
- Apply observational study methods to evaluate non-randomized care pathways and quantify counterfactual patient outcomes.
- Identify critical clinical touchpoints and dosage adjustment milestones that drive long-term program adherence.
- Partner with the clinical research team to provide analytical support for publications and studies.
- Translate analytical findings, treatment protocol insights, and patient drop-off trends into actionable workflow improvements.
Requirements
- Advanced proficiency in SQL for data manipulation.
- Strong Python skills for predictive modeling and data transformation.
- Practical experience applying clustering, regression/classification, and survival analysis.
- Hands-on experience with NLP techniques and LLM frameworks.
- Solid working knowledge of quasi-experimental design, propensity score matching, and counterfactual reasoning.
- Comfort working with messy, semi-structured, or missing healthcare datasets.
- Ability to translate technical modeling outcomes into clear, intuitive insights for Clinical Operations and Product partners.
- Candidates must be authorized to work in the U.S. without current or future sponsorship needs.
- All Candidates must be authorized to work in the United States without current or future sponsorship needs.
Skills
- SQL
- Python
- Clustering
- Segmentation
- Regression
- Classification
- Survival Analysis
- scikit-learn
- statsmodels
- lifelines
- NLP
- LLM frameworks
- transformers
- Claude API
- Causal Inference
- Quasi-experimental design
- Propensity Score Matching
- Counterfactual reasoning
- Healthcare Data
- Lab results
- PROMs
- EHR notes
- JSON
- Feature structuring
- Model validation
- Cross-functional communication
Location
- Palo Alto
- San Francisco
Work Type
- Hybrid
- Full-time W2
Experience Level
- 2 to 4 years of hands-on data science or analytics experience delivering high-impact solutions.
Education Level
- Bachelor’s or Master’s degree in Biomedical Data Science, Health Informatics, Biostatistics, or a related quantitative field.
Salary/Compensations
- 144K-172K
Benefits
- Competitive base salary
- Equity
- Benefits
About the Company
- Midi Health is 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.
Equal Opportunity
- Midi Health is an Equal Opportunity Employer.