Data Scientist, Clinical Outcomes

Midi Health•Palo Alto, CA

2h•$144,000 - $172,000•Hybrid

About The Position

Midi is seeking a pragmatic, hands-on Data Scientist to build and own the quantitative foundation supporting clinical care delivery, refinement of patient treatment plans, and retention. The role connects clinical outcomes with business growth by helping match patients to personalized treatment paths that can improve clinical efficacy, satisfaction, and long-term retention. The Data Scientist will work with structured clinical metrics—including lab values, symptom severity scores, and dosage trajectories—as well as unstructured patient communications such as visit transcripts, ambient scribe outputs, and intake survey dialogue. Working across Data Science, Clinical Operations, and Product, this person will transform messy, multimodal patient data into clinical decision support and personalized care pathways. The position is hybrid in Palo Alto or San Francisco, with two days per week in the office. It is a full-time W2 role reporting to the Director Data Science + Analytics. The base salary range is $144,000–$172,000, depending on experience, plus equity and benefits. Midi is unable to provide visa sponsorship; candidates must be authorized to work in the United States without current or future sponsorship needs.

Requirements

Advanced proficiency in SQL and strong Python skills for predictive modeling and data transformation.
Practical experience with clustering, regression or classification, and survival analysis, using tools such as scikit-learn, statsmodels, or lifelines.
Hands-on experience with NLP techniques and LLM frameworks, such as transformers or the Claude API, for parsing, extracting, and classifying unstructured text.
Solid working knowledge of quasi-experimental design, propensity score matching, and counterfactual reasoning using observational datasets.
Comfort working with messy, semi-structured, or missing healthcare data, including lab results, Patient-Reported Outcome Measures (PROMs), and EHR notes.
A pragmatic builder mindset and willingness to perform foundational data architecture work, including cleaning JSON outputs, structuring features, and validating baseline models.
Ability to communicate modeling outcomes as clear, intuitive insights for Clinical Operations and Product partners.
Two to four years of hands-on data science or analytics experience delivering high-impact solutions.
Experience working with healthcare, telehealth, or digital health datasets.
Demonstrated experience parsing unstructured text or transcript data into structured modeling pipelines.
Bachelor’s or Master’s degree in Biomedical Data Science, Health Informatics, Biostatistics, or a related quantitative field.
Must be authorized to work in the United States without current or future visa sponsorship.

Responsibilities

Build reliable modeling tables and feature stores by parsing, cleaning, and joining clinical data such as lab results, symptom severity surveys, medication history, and raw visit transcripts.
Design data-processing pipelines that convert unstructured clinical notes, orders, and visit transcripts into structured, queryable relational tables.
Build lightweight NLP and LLM-based pipelines to extract patient intent and preferences from visit transcripts, including preferences about treatment pathways, communication, and modality.
Analyze patient dialogue to detect unaddressed obstacles and early side-effect reports that could lead to treatment discontinuation.
Develop unsupervised segmentation models to group patients into lookalike cohorts based on baseline lab values, symptom profiles, and transcript-derived preferences.
Build predictive models to estimate symptom reduction, time-to-relief, and potential side-effect risks across alternative treatment pathways.
Apply observational study methods, including propensity score matching, difference-in-differences, and survival analysis, to evaluate non-randomized care pathways and estimate counterfactual outcomes.
Identify clinical touchpoints and dosage adjustment milestones that influence program adherence, and connect milestone achievement with patient retention and lifetime value.
Partner with the clinical research team to support peer-reviewed medical publications, white papers, and payer studies.
Translate analytical findings, treatment protocol insights, and patient drop-off trends into actionable workflow improvements for clinical operations and care teams.