BASIC FUNCTION:

The Department of Internal Medicine is seeking a Biostatistician to support a collaborative program of health data science and clinical research. The successful candidate will contribute to the design, analysis, and reporting of studies that span clinical trials and observational research, with particular emphasis on the secondary analysis of large health datasets, including administrative claims data such as the Merative MarketScan Research Databases. Working closely with investigators, clinicians, epidemiologists, and data scientists, this person will help turn complex data into rigorous, reproducible, and actionable evidence.

KEY RESPONSIBILITIES:

• Collaborate with investigators across the full research lifecycle: study design, analysis planning, execution, interpretation, and dissemination.

• Construct, clean, and manage analytic datasets from large and complex sources, including administrative claims data (e.g., Merative MarketScan), electronic health record (EHR)-derived data, registries, and clinical trial data.

• Develop cohort definitions and code-based algorithms (e.g., ICD, CPT/HCPCS, NDC) to identify patients, exposures, and outcomes.

• Apply appropriate statistical methods for both experimental and observational data, including regression modeling, survival analysis, methods for confounding adjustment, and approaches for handling missing data.

• Write statistical analysis plans; produce tables, listings, and figures; and contribute to manuscripts, abstracts, grant proposals, and reports.

• Program primarily in R and SAS, developing reusable, well-documented, and reproducible code.

• Perform sample size and power calculations to support study planning.

• Communicate methods and findings clearly to both statistical and non-statistical audiences.

• Use modern tools, including AI/LLM-based coding assistants, to improve productivity while adhering to data governance, privacy (e.g., HIPAA), and responsible-use standards.

Required Qualifications:

• Master’s degree in biostatistics (or related field) or an equivalent combination of education and experience.
• At least 6–12 months of post-graduate experience working with large health-related datasets (e.g., administrative claims, EHR-derived data, or registries).
• Proficiency in both R and SAS programming (minimum of 1 year of experience each), including data manipulation (e.g., SAS data step programming and R data import/management) and knowledge of appropriate procedures for statistical analyses and data presentation.
• Knowledge of relational database methods to manipulate and merge multiple large datasets into analytic datasets.

Desired Qualifications:

• Demonstrated understanding of statistical analysis procedures and interpretation of analysis results.
• Strong written and verbal communication skills, with a demonstrated ability to convey technical content to different audiences.
• Demonstrated ability to collaborate effectively within a multidisciplinary team environment.
• Good organizational skills, attention to detail, and the ability to manage multiple deadlines.
• Experience working with large administrative claims or other real-world datasets, especially Merative MarketScan (e.g., Commercial Claims and Encounters, Medicare Supplemental, and/or Medicaid databases), but also Optum, CMS Medicare, EHR-derived datasets, HCUP, Premier, etc.
• Experience and background in clinical trials research through graduate-level coursework or project experience.
• Experience writing statistical analysis plans, analysis reports, protocols, or methods sections.
• Development of reports, tables, and listings in a professional setting.
• Proficiency with macros and/or functions for reusable code in SAS or R.
• Use of SAS and/or SQL to navigate relational databases.
• Experience with reproducible research workflows (e.g., R Markdown/Quarto) and version control (e.g., Git).
• Experience with advanced modeling using actual research data (e.g., survival analysis, longitudinal/mixed-effects models, propensity score methods, or other causal inference approaches).
• Coursework and/or professional experience with missing data techniques.
• Coursework and/or professional experience with randomization procedures and sequence generation.
• A record of contribution to peer-reviewed publications or professional conference presentations.

Position and Application Details:

In order to be considered for an interview, applicants must upload the following documents and mark them as a “Relevant File” to the submission:

• Resume
• Cover Letter

Job openings are posted for a minimum of 7 calendar days and may be removed from posting and filled any time after the original posting period has ended.

Successful candidates will be required to self-disclose any conviction history and will be subject to a criminal background check and credential/education verification. Up to 5 professional references will be requested at a later step in the recruitment process.

For additional questions, please contact ashley-rayer@uiowa.edu.