Data Scientist
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Requirements
• Bachelor's degree in Computer Science, Data Science, Statistics, Biostatistics, Mathematics, or a related quantitative field (required) • Master's degree in Biostatistics, Statistics, or a related discipline strongly preferred — or a CS/Data Science degree with significant coursework in statistics, causal inference, and study design • 1-3 years of professional experience in data science, statistical analysis, or quantitative research (graduate research, thesis work, or internships count) • Strong foundation in statistical methods: regression, hypothesis testing, study design • Familiarity with causal inference concepts and when they apply — through coursework, research, or professional experience • Ability to assess data quality, identify inconsistencies, and understand how upstream issues affect downstream results • Python and SQL proficiency — you will build and maintain analysis pipelines, not just run ad-hoc queries. We will train you on our stack (PySpark, BigQuery, GCS), but you should be comfortable writing and reviewing code in a collaborative engineering environment. • Experience working with messy, real-world datasets (healthcare claims, EHR, insurance, government/public-use data, etc.) • Familiarity with healthcare data concepts (diagnosis codes, episode grouping, cost metrics, FHIR) • Experience with machine learning (scikit-learn, PyTorch, Spark ML, or similar) • Experience with survival analysis or longitudinal data methods • Exposure to health economics, health services research, or outcomes research — through coursework, research, or industry experience • Comfort using AI tools to improve productivity (e.g., Claude, Cursor) • Core Attributes • Core Attributes • Attention to detail: You validate results against expectations and don't hand off work you haven't double-checked. • Eagerness to learn: You seek out what you don't know rather than waiting to be taught. • Reliability: You follow through on commitments and communicate early when things aren't going as planned. • Curiosity: You want to understand the problem, not just complete the task. You ask why, dig into unexpected results, and aren't satisfied with an answer that doesn't make sense. • Communication: You can explain your methodology and results clearly to both technical and non-technical audiences. • Location • This is a remote position based in the United States
Responsibilities
• Execute and contribute to the design of observational studies using claims data (e.g., case-control matching, difference-in-differences, propensity score methods) • Conduct analyses to measure the cost and outcomes impact of Solera's programs • Investigate product and engagement data to identify patterns, drop-off points, and opportunities to improve outcomes • Build and maintain analysis and modeling pipelines in Python and Spark for feature engineering, cohort construction, and outcomes measurement • Contribute to the team's ML products (e.g., risk models, patient matching) through feature development, evaluation, and iteration • Collaborate cross-functionally with health economics, clinical, product, sales, and engineering teams to interpret results and deliver actionable insights • Document methodologies and findings clearly enough to withstand external scrutiny • Work with cloud data infrastructure (BigQuery, GCS, Dataproc) to query, transform, and analyze large datasets
Benefits
• Competitive salary • Comprehensive medical, dental, and vision coverage starting Day One • Flexible time off beyond standard company holidays • 401(k) with company match and financial wellness resources • Generous parental leave and adoption assistance • Remote-first work culture with team events and community building • Phone stipend and wellness perks (Fitbit, fitness/mental health app access) • Work AuthorizationApplicants must be currently authorized to work in the United States on a full-time basis. Solera Health is not able to sponsor or transfer employment visas for this position. • Work Authorization • Disclaimer: The information contained herein is not intended to be an all-inclusive list of the duties and responsibilities of the job, nor are they intended to be an all-inclusive list of the skills and abilities required to do the job. Management may, at its discretion, assign or reassign duties and responsibilities to this job at any time.