Bioinformatics Research Analyst - Department of Oncology

Bioinformatics Research Analyst - Department of Oncology

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. The position reports to the Professor, Oncology.

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.

Unique duties:

Qualifications:

Education: Master's degree

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.

Experience: Entry level (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.

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.

Knowledge, Skills, and Abilities:

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.

Familiarity with large genomic and population resources such as All of Us, UK Biobank, dbGaP, gnomAD, 1000 Genomes and TOPMed.

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.

Familiarity with integration and interpretation of multiple genomic data types, including sequencing, transcriptomic, epigenomic and chromatin-accessibility data.

Strong written and verbal communication skills and the ability to contribute to scientific presentations, manuscripts, abstracts, and collaborative research discussions.

Preferred qualifications:

School/College/Division:

H06 - School of Medicine

Primary department:

H0632 - Oncology

Employment type:

• Regular Employee
• Job type: Full Time
• Job category: Research

Funding/salary information:

• Compensation type: Annual Salary

Working conditions:

Normal office and computational research environments.

Job openings:

• Number of openings: 1
• Reposted position: No
• Reposted reason: None (New Requisition)
• Prior posting/requisition number:

Background check requirements:

University policy requires certain persons who are offered employment to undergo a background check, including a criminal history check, before starting work. If you are offered employment, the university will inform you if a background check is required.