Bioinformatics Research Analyst - Department of Oncology

Wayne State University • Detroit, MI

6d • Onsite

About The Position

Wayne State University is searching for an experienced Bioinformatics Research Analyst - Department of Oncology at its Detroit campus location. Wayne State is a premier, public, urban research university located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity, diversity and inclusion creates exceptional educational opportunities which prepare students for success in a global society. Essential functions (job duties): Provide computational and analytic support for a cancer genetic epidemiology research program focused on genomic determinants of cancer susceptibility and outcomes. Under the direction of the Principal Investigator, develop and execute reproducible workflows for large-scale genomic, EHR-linked, and multi-omic data; perform statistical genetic analyses; interpret results; and contribute to collaborative scientific outputs.

Requirements

• Master’s degree from an accredited college or university in Bioinformatics, Computational Biology, Statistical Genetics, Genetic Epidemiology, Biostatistics, Data Science, or a related quantitative field.
• One year of job-related experience.
• Prior research experience analyzing human genomic or other high-dimensional biological data including quality control, analysis, and interpretation of human genetic data derived from whole-genome sequencing, whole-exome sequencing, array genotyping, and/or other genomic technologies required.
• Experience performing genomic data quality control, data harmonization, variant annotation, and statistical analysis required.
• Proficiency in R for data manipulation, statistical analysis, and visualization.
• Working knowledge of Unix/Linux, shell scripting, and command-line bioinformatics tools.
• Familiarity with common human-genetics data formats, including VCF/BCF, PLINK BED/BIM/FAM or PGEN/PVAR/PSAM, BED interval files and GWAS summary-statistic files.
• Ability to work with large-scale genomic datasets in cloud-computing or high-performance computing environments, including efficient management and analysis of datasets that cannot be processed locally.
• Knowledge of human genetic association methods, including single-variant association testing, gene- or region-based analysis, population stratification, and ancestry adjustment, GWAS, rare-variant analysis and interpretation of association results.
• Ability to develop clear, reproducible, well-documented analytic code and computational workflows, including appropriate quality control and validation steps.
• Strong quantitative reasoning and troubleshooting skills, including the ability to identify and resolve problems involving data structure, computational pipelines, statistical models, and unexpected analytic results.
• Ability to critically interpret genetic and genomic results and communicate findings clearly to the Principal Investigator, scientific collaborators, and multidisciplinary research teams.
• Ability to manage multiple analyses simultaneously, maintain organized analytic documentation, meet project deadlines, and adapt to evolving research questions and methods.
• Ability and willingness to independently learn new statistical, computational and bioinformatic methods as required by research projects.
• Strong written and verbal communication skills and the ability to contribute to scientific presentations, manuscripts, abstracts, and collaborative research discussions.

Nice To Haves

• Experience working with large population or clinical research datasets, including EHR-linked genomic cohorts preferred.
• Graduate research and internships experience preferred; MS research project experience is acceptable.
• Experience with Hail, SQL or relational databases, Git/version control, cloud-based genomic analysis, PRS, Mendelian randomization, gene-environment interaction analysis or multi-omics integration preferred.
• Experience with Python preferred.
• Familiarity with large genomic and population resources such as All of Us, UK Biobank, dbGaP, gnomAD, 1000 Genomes and TOPMed.
• Familiarity with integration and interpretation of multiple genomic data types, including sequencing, transcriptomic, epigenomic and chromatin-accessibility data.

Responsibilities

• Develop, implement, and maintain reproducible computational workflows for analysis of large-scale human genomic data, including whole-genome/whole-exome sequencing, array-based genotype data, and derived genomic datasets.
• Perform data quality control, extraction, transformation, and integration across genomic, phenotype/EHR and multi-omic datasets using secure cloud and high-performance computing environments, including Hail/VDS-based workflows when appropriate.
• Conduct statistical genetics and genetic epidemiology analyses under PI direction, including variant- and region-based association analyses, population-stratified analyses, risk-score analyses, and related methods appropriate to individual projects.
• Annotate and interpret genomic findings using internal and public reference resources; evaluate technical and biological plausibility and summarize results for scientific decision-making.
• Maintain organized, documented, version-controlled code and analytic records; perform validation and troubleshooting to support reproducibility and efficient reuse of pipelines across projects.
• Prepare analysis summaries, tables, figures, methods documentation and other materials for manuscripts, abstracts, presentations, grants, and project reports.
• Perform other related duties as assigned.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level