Data Analyst

Department
BSD HGD - Unassigned Lab
About The Department
The newly established Chen Lab (https://siwei-lab.org/) is based in the Department of Human Genetics at the University of Chicago. Our research strives to catalyze repeated traversal of the 'genomic medicine cycle,' driving the discovery, biological understanding, and clinical translation of the genetic underpinnings of human disease. Our lab plays a leading role in multiple international consortia, including Epi25, the International League Against Epilepsy (ILAE), and the Genome Aggregation Database (gnomAD). Leveraging advances in genomics technologies, we have made seminal discoveries that elucidate the genetic basis underlying conditions ranging from severe neurodevelopmental disease to population-level phenotypic variation. Our work has been published in high-profile journals including Nature, Nature Genetics, Nature Neuroscience, and others. We are currently expanding efforts to build large-scale data commons for human complex disorders and to integrate emerging technologies such as AI to drive the next wave of genomic and biomedical discovery.
Job Summary
We are seeking a highly motivated and detail-oriented Data Analyst to join our team in advancing large-scale genomic studies. There are multiple (non-exclusive) exciting projects you may contribute to:

Genetic association analysis to discover genes and mutations driving disease risk.
Multi-omics analysis to reveal biological processes underlying disease phenotypes.
Statistical and AI/ML method development to enhance and expand the approaches above.
Software and web platform development to support the dissemination of findings.
All of these projects are supported by well-funded international consortia (Epi25, ILAE, and gnomAD) where we lead flagship projects that have generated highly impactful and collaborative science over the years. The overarching goal is to better understand the genetic basis of human diseases and translate discoveries into novel therapeutic strategies.
As a member of our team, you will gain hands-on experience working with real-world data at the population level and/or in clinical setting. These datasets include genomic and phenotypic information from hundreds of thousands of individuals and represent several of the largest initiatives of their kind worldwide. You will have the opportunity to collaborate with an international, interdisciplinary network of leading researchers and clinicians and to produce results with direct clinical and public health implications.
Responsibilities

Discusses, plans, and carries out research in a stimulating and collaborative environment.
Processes and analyzes large-scale genomics datasets for disease association studies.
Develops and/or applies statistical and AI/ML models for biologically informed gene discovery and variant interpretation.
Builds and implements computational workflows for data quality control, annotation, and downstream analyses.
Summarizes findings in reports, manuscripts, and presentations for internal and external dissemination.
Maintains well-documented, reproducible code and workflows.
Assists in analyzing data for the purpose of extracting applicable information. Performs research projects that provide analysis for a number of programs and initiatives.
May assist staff or faculty members with data manipulation, statistical applications, programming, analysis and modeling on a scheduled or ad-hoc basis.
Collects, organizes, and may analyze information from the University's various internal data systems as well as from external sources.
Maintains and analyzes statistical models using general knowledge of best practices in machine learning and statistical inference. Performs maintenance on large and complex research and administrative datasets. Responds to requests and engages other IT resources as needed.
Performs other related work as needed.
Education:
Minimum Qualifications
Minimum requirements include a college or university degree in related field.
Work Experience:
Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.
Certifications:
Preferred Qualifications
Education:

Bachelor’s degree or higher in computational biology, bioinformatics, statistics, computer science, AI/ML, or a related quantitative field.
Technical Skills Or Knowledge:

Basic knowledge of genetics principles.
Proficiency in programming languages and working with high-performance or cloud computing environments.
Practical experience in either large-scale genomics data analysis or statistical/AI/ML method application to biological data.
Familiarity with omics datasets and modern statistical/AI/ML methodologies in biology.
Preferred Competencies

Strong organizational skills, attention to detail, and work independently and in a team setting.
Proactive mindset with strong communication skills to work effectively in an interdisciplinary team.
Enthusiasm for initiating innovative secondary analyses, such as integration with multimodal functional and clinical data from external resources.
Application Documents

Resume/CV (required)
Cover Letter (required)
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
Hourly
FLSA Status
Non-Exempt
Pay Range
$24.04 - $31.25
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.
Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
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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