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.

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 years of work experience in a related job discipline.

Certifications:

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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 expe