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About the Role
Support research projects by transforming source data into analytic-ready datasets, performing statistical analyses, and generating reports. Work with small teams of epidemiologists and statisticians, focusing on executing study protocols and building reproducible analytical pipelines.
Responsibilities
- Write R code to execute research projects, build cohorts, and run analyses.
- Coordinate through GitLab and maintain close contact with team members.
- Work with deidentified administrative claims and electronic health record (EHR) data.
- Draw on domain knowledge of database conventions to build study variables.
- Use dbplyr to query source databases from R, generating SQL.
- Apply eligibility criteria from a study protocol and/or SAP to identify the study population.
- Determine the index date and start of follow-up for each patient.
- Build the analytic data set, typically one row per patient with relevant columns.
- Fit models specified in the study design, potentially using internal packages for causal inference.
- Run diagnostics on the analysis, such as checking propensity score distributions, weight behavior, and covariate balance.
- Produce tables and figures for study results, including baseline characteristics, effect estimates, and risk curves.
- Assemble results into client deliverables.
- Build project repositories as orchestrated pipelines that reliably reproduce study results.
- Participate in quality control procedures, including code review, testing, and double coding.
- Work closely with the study design team throughout the study lifecycle, incorporating changing requirements and providing feedback.
Requirements
- Master's degree, or two or more years of relevant work experience, in biostatistics, epidemiology, health economics, bioinformatics, data science, or a related quantitative field.
- Strong programming skills in R.
- Experience querying relational databases, whether directly in SQL or through an interface like dbplyr.
- Experience writing code that others can read, run, and reproduce.
- Strong communication, presentation, and collaboration skills with cross-functional technical and scientific teams.
Skills
- R
- SQL
- dbplyr
- GitLab
- Causal inference
- Epidemiology
- Biostatistics
- Data science
- Health economics
- Bioinformatics
- Real-world healthcare data (administrative claims, EHR, hospital chargemaster, registry data)
- Longitudinal data set construction
Location
- Hybrid
Work Type
- Hybrid
- Full-time
Experience Level
- Master's degree or 2+ years relevant experience
Education Level
- Master's degree
Benefits
- Comprehensive health, dental, and vision coverage
- 401(k) with company match
- Generous PTO
- Company holidays
- Paid parental leave
About the Company
- Headwater Science (formerly NoviSci) is a data science and methods company specializing in principled, reproducible evidence generation for complex clinical and regulatory challenges.
- Deep expertise in comparative effectiveness, causal inference, healthcare utilization and expenditure research, and regulatory-grade analytical software.
- Provides the methodological foundation that delivers reproducible analytic pipelines, novel epidemiologic and statistical methods, and regulatory-grade software validated to hold up under the most demanding scrutiny.
- Works with life sciences organizations as a long-term scientific partner.
- Headwater Science is a Highlander Health company.