Data Scientist

About Headwater Science

Headwater Science (formerly NoviSci) is a data science and methods company specializing in principled, reproducible evidence generation for complex clinical and regulatory challenges. With deep expertise in comparative effectiveness, causal inference, healthcare utilization and expenditure research, and regulatory-grade analytical software, Headwater Science 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. The company works with life sciences organizations as a long-term scientific partner. Headwater Science is a Highlander Health company. Learn more at.

The Role

In this role, you will support our research projects by helping to transform source data from healthcare databases into analytic-ready data sets, performing statistical analyses, and generating reports of the results. You will work with small teams of epidemiologists and statisticians whose responsibilities span study design and execution. Your work will focus primarily on executing specifications outlined in study protocols/statistical analysis plans (SAP) and building reproducible analytical pipelines that are understandable, well documented, and compliant with quality control standards. Typical projects include studies of natural history, treatment patterns, and comparative effectiveness/safety, often incorporating negative controls to evaluate treatment group comparability.

What You’ll Do

How We Work: You'll spend most of your time writing R code to execute research projects — building cohorts and running the analyses that produce results. You'll work alongside team members, coordinating through GitLab and staying in close contact as the code comes together. Project code runs on remote servers where the data live — usually ours, but sometimes a client or data provider environment. A research project moves through two main phases — cohort building, then analysis and reporting — and you'll work in both.

Cohort Building: This is the largest piece of the role – turning raw healthcare databases into analytic-ready data sets. You will work with deidentified administrative claims and electronic health record (EHR) data, which come as relational tables covering patient demographics, diagnoses, procedures, medications, and lab results. Each data source has its own conventions, and learning them is critical to being successful in this role. In practice, you will:

• Draw on domain knowledge of how each database represents clinical events to make appropriate choices when building study variables.
• Use dbplyr to query source databases from R, generating SQL against tables that are often too large to hold in memory.
• 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 — often one row per patient, with columns for baseline characteristics, changes in treatment and health status over time, outcomes of interest, and censoring.

Analysis and Reporting: Our statisticians often lead this phase of research projects, with you in a supporting role — although this arrangement may vary depending on the study and your experience level. You will extend the project pipeline to take the analytic-ready data set through to the finished report. In practice, you will:

• Fit the models specified in the study design, often using causalRisk, our internal package for causal inference with observational data.
• Run diagnostics on the analysis — such as checking propensity score distributions, weight behavior, and covariate balance — and flag problems back to the study design team.
• Produce the tables and figures that make up the study results — baseline characteristics, effect estimates, and study-specific outputs like risk curves and treatment patterns — and assemble them into client deliverables.

Collaboration and Quality Control: Across both phases of a research project, you will:

• Build project repositories as orchestrated pipelines that reliably reproduce the study results each time they are run.
• Participate in quality control procedures including code review, testing, and double coding of portions of an analysis as an independent check on the primary implementation.
• Work closely with the study design team throughout the study lifecycle, incorporating changing requirements, sharing interim results, and providing feedback on how well the study design suits the available data.

What You’ll Bring

• 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.

Nice to Have

• Experience with real-world healthcare data, such as administrative claims, EHR, hospital chargemaster, or registry data.
• Experience constructing longitudinal data sets for observational research.

What We Offer

• Hybrid work- 3 days/week in office to collaborate with the team
• Comprehensive health, dental, and vision coverage for you and your family.
• 401(k) with company match.
• Generous PTO and company holidays.
• Paid parental leave.

If you are ready to be part of a team where your work truly matters—where your expertise is valued, your growth is supported, and your contributions help shape the future of healthcare—Headwater Science is the place for you. We’re building something meaningful together, and we’d love for you to be a part of it.