Data Scientist

Job Summary

Solera is seeking a Data Scientist to join our Insights team. You will work with large-scale claims and product engagement data to answer questions that matter to the business: Does our product improve health outcomes? How much does it save? Where are patients disengaging, and why? Your analyses will directly support enterprise sales, inform product decisions, and shape company strategy. You will work closely with our health economics team to design and execute rigorous analyses that demonstrate real-world value to customers. 

This is a high-impact role on a small team where your work directly influences business outcomes and product direction. You will be mentored directly by the team lead, with increasing ownership as you grow. 

Key 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 

Education & Experience 

• 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. 

Preferred Qualifications 

• 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

• 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

What We Offer

• Competitive salary