Research Software Engineer

Department

BSD MED - Center for Health Statistics

About the Department

The Center for Health Statistics (CHS) was established in 2001 by Robert D. Gibbons, Blum-Riese Professor of Statistics at the University of Chicago, to provide a statistical research environment that would lead to advances in the design and analysis of investigations in a wide variety of areas in the health sciences. The Center has quickly grown to include a faculty of 32 distinguished members covering statistical expertise in almost all areas of science. Focus has extended to the social, behavioral and physical sciences in addition to problems in our nation’s health. Key statistical themes in our work include: (a) generalized mixed-effects regression models for the analysis of clustered and longitudinal data; (b) item response theory and adaptive testing for large-scale educational testing and mental health screening and monitoring; (c) environmental statistics, both regulatory and health related; and (d) drug safety. Overall, we are committed to working on statistical features of any interesting scientific problem, and our excellent faculty provide the depth of resources to accomplish this goal.

Job Summary

The Center for Health Statistics (CHS) within the Biological Sciences Division at the University of Chicago is seeking a Research Software Engineer with strong data engineering and DevOps capabilities to support research computing needs. This role centers on designing systems that link analytical methods with production environments, enabling scalable, reliable, and reproducible workflows for CHS research groups. This position provides technical leadership across data architecture, automation, and deployment, contributing to the development lifecycle for projects that rely on advanced analytics and machine learning, including the development, deployment, and maintenance of complex statistical research products such as computerized adaptive tests (CATs), psychometric scoring engines, and assessment workflows.

Responsibilities

• Partners with investigators and analysts to design and build data pipelines that support large-scale statistical and machine-learning workflows with psychometric applications.
• Develops, automates, and maintains infrastructure for data ingestion, transformation, validation, and monitoring across multiple research studies with an emphasis on item-level data processing, calibration pipelines, and scoring services.
• Implements deployment strategies (CI/CD, containerization, orchestration) that ensure reproducibility, version control, and efficient delivery of computational tools used in adaptive testing and statistical measurement systems.
• Collaborates with research teams to integrate modeling outputs into operational systems, dashboards, or downstream applications.
• Maintains secure, compliant, and well-documented environments aligned with University guidelines and relevant data-use regulations related to human-subjects research data.
• Supports cloud computing resources, including performance optimization, resource provisioning, and cost monitoring.
• Establishes testing frameworks and code-quality practices for shared research software with rigorous validation of psychometric algorithms and adaptive-testing logic.
• Contributes to technical documentation, training materials, and knowledge transfer to CHS staff and collaborators.
• Assists with technical sections of grant proposals, progress reports, and presentations by compiling system architecture descriptions, performance metrics, or reproducibility details.
• Works with stakeholders to gather and analyze requirements for developmental programs. Receives a moderate level of guidance to design applications to meet University and business requirements.
• Supports and maintains existing applications. Works with web developers and responds to requests from users.
• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• Advanced degree in computer science, data science, engineering, or a related technical field.

Experience:

• Background developing data systems or research infrastructure in scientific, academic, or healthcare environments.

Technical Skills or Knowledge:

• Proficiency in building and maintaining data engineering solutions, such as distributed processing, workflow orchestrat