Senior Biomedical Informatics Programmer

Department

BSD MED - General Internal Medicine

About the Department

The Precision Health Informatics Lab (PHIL), led by Dr. Joshua Denny, MD, MS, develops and applies computational and informatics methods to translate large-scale genomic and clinical data into actionable insights about human health and disease. The lab's work sits at the intersection of genomics, electronic health record (EHR) data, and population-scale biobank research, with a focus on discovering gene-disease relationships, characterizing the clinical effects of genetic variation, and enabling precision medicine applications.

A growing part of this work involves machine learning, large language models, and generative AI, which the lab uses to extract identify patterns across heterogeneous data sources and accelerate the pace of genomic discovery.

A central focus of the lab is phenomics, the integrative use of EHR-derived and survey-based phenotype data to power genetic and multiomic discovery. The lab develops and applies methods such as phenome-wide association studies (PheWAS), which test genetic variants against the full breadth of clinical phenotypes captured in health records, as well as traditional genome-wide association study (GWAS) approaches on complex phenotypes.

PHIL builds and maintains computable phenotype algorithms, reproducible, data-driven definitions of disease states, drug effects, and other clinical conditions, that integrate EHR data, survey responses, and other health data sources.

We also seek to use large scale biobank data to uncover novel gene functions and previously unrecognized genetic diseases, an undertaking that depends on integrating genomic data with clinical, phenotypic, and knowledge-base resources at scale. PHIL collaborates broadly with clinical, genomic, and computational investigators to support biobank-scale studies, and is building a team of programmers, analysts, and scientists to sustain and expand this work.

Job Summary

The Senior Biomedical Informatics Programmer is integral to the ongoing operations of the Precision Health Informatics Lab (PHIL), providing critical scientific and bioinformatics programming support to enable large-scale biobank studies. This role will design, optimize, and maintain production-grade pipelines that connect genomic, phenotypic, and clinical data to support gene-disease discovery at scale, including studies using deep clinical characterization of individuals with rare genetic variants to uncover novel gene functions and

genetic diseases.

Responsibilities

• Optimize and maintain best-practice pipelines for gene-disease association analyses, including genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS).
• Build and refine computable phenotype algorithms for diseases, drug effects, and other clinical conditions by integrating EHR data with survey-based data sources.
• Develop scripts, pipelines, and software to analyze diverse health-related datasets, combining data across orthogonal sources — including EHR data, environmental exposures, and taxonomic/knowledge resources such as OMIM, the Human Phenotype Ontology, and DrugBank — to support genomic analyses.
• Provide broad-based programming and analytic support across a range of informatics and genomics research projects, including custom script development for analysis of public datasets and lab-generated data.
• Support the computational infrastructure underlying studies of rare genetic variants, helping enable discovery of novel gene functions and genetic diseases through clinical characterization.
• Collaborate with scientists, clinicians, and analysts to ensure pipelines are reliable, reproducible, and scalable for biobank-level data volumes.
• Generate public use resources.
• Leads implementation or modification of existing web-based bioinformatics tools.
• Provides expertise to biomedical informatics related concepts, applications, and tools.
• 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 7+ years of work experience in a related job discipline.
• Certifications: ---

Preferred Qualifications

• Education: Advanced degree (MS or PhD preferred) in bioinformatics, computer science, computational biology, or related field.
• Experience: Strong programming skills (e.g., Python, R, or similar) and experience building production data pipelines.
• Experience with genomic and/or EHR data formats and analysis (e.g., VCF, FHIR/OMOP, PheWAS/GWAS methods).
• Familiarity with biomedical ontologies and knowledge bases (OMIM, HPO, DrugBank).
• Experience working with large-scale biobank or clinical datasets.
• Preferred Competencies
• Excellent verbal and written communication skills.
• Excellent interpersonal and customer service skills.
• Excellent organizational skills, ability to maintain files.
• Proficiency with word processing applications, preparing presentations, spreadsheets and databases.
• Ability to maintain calm and courteous demeanor and to work productively despite heavy workload, competing priorities, complex problems, and tight deadlines.
• Ability to exercise sound judgment and excellent problem-solving skills.
• Ability to exercise flexibility and contribute to the overall work of the team.
• Considerable skill in working both independently and as a team member.
• Ability to take initiative.
• Application Documents
• Resume/CV (required)
• Cover Letter (required)
• The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.
• When applying, the document(s) MUST  be uploaded via the My Experience page, in the section titled Application Documents of the application.
• Job Family
• Research
• Role Impact
• Individual Contributor
• Scheduled Weekly Hours
• 40
• Drug Test Required
• No
• Health Screen Required
• No
• Motor Vehicle Record Inquiry Required
• No
• Pay Rate Type