Staff Scientist

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 Staff Scientist will contribute scientific leadership and hands-on research expertise to the Precision Health Informatics Lab (PHIL), supporting the design, execution, and interpretation of large-scale genomic and phenomic studies. This role will play a key part in advancing biobank-scale research into rare genetic variants and their clinical effects, bridging computational methods development with biological and clinical interpretation.

Responsibilities

• Design and lead analytic strategies for gene-disease discovery studies, including GWAS, PheWAS, and integrative multi-omic approaches.
• Guide the development of computable phenotype definitions for diseases, drug effects, and other conditions, ensuring clinical and biological validity of EHR- and survey-derived phenotypes.
• Interpret findings from genomic and phenomic analyses in the context of existing biomedical knowledge (e.g., OMIM, Human Phenotype Ontology, DrugBank) to prioritize novel gene-disease and gene-function hypotheses.
• Contribute to the scientific direction of studies identifying and characterizing novel genetic diseases and gene effects through clinical investigation of individuals with rare variants.
• Mentor and collaborate with programmers, analysts, and trainees on study design, statistical approaches, and biological interpretation of results.
• Prepare manuscripts, reports, grants, and presentations communicating research findings to scientific and clinical audiences.
• Stay current with emerging methods in statistical genetics, phenomics, and precision medicine to inform lab research directions.
• Serves as a resource for collecting data and performing analysis. Facilitates and promotes a research project by providing scientific or intellectual information.
• Creates first drafts for scientific writing and publications, including protocols and grants.
• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a PhD in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• PhD in genetics, genomics, bioinformatics, epidemiology, or related field.

Experience:

• Demonstrated research experience in human genetics, genomic epidemiology, or clinical informatics.
• Strong understanding of statistical genetics methods (GWAS, PheWAS, or related approaches).
• Experience working with EHR-linked biobank data.
• Track record of scientific publication and ability to communicate complex findings clearly.

Preferred Competencies

• Proven track record of scientific publications and research contributions.
• Excellent written and verbal communication skills.
• Ability to work independently and collaboratively in a multidisciplinary environment.
• Strong manuscript and proposal writing skills.
• Ability to work with people from different backgrounds.
• Strong problem-solving skills.
  

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

Salary

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FLSA Status

Exempt

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Pay Range

$80,000.00 - $100,000.00

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

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