Bioinformatician II - Windreich Department of AI & Human Health

Responsibilities

• Perform large-scale genetic analyses, including QC pipelines, GWAS, rare variant association analyses, PRS construction, fine-mapping, proteomics, and integrative genomics.
• Strong experience with genetic analyses tools including PLINK, SAIGE, Regenie, BOLT-LMM, GCTA, LDSC, KING, bcftools, samtools, vcftools.
• Conduct proteomic and multi-omics analyses, including pQTL mapping, protein–trait associations, and pathway-level interpretation.
• Support investigators by designing, executing, and maintaining reproducible analytical workflows using RStudio, Python, and shell scripting.
• Develop and maintain computational pipelines in JupyterLab or similar tools for scalable analyses.
• Manage, explore, and analyze EHR-linked clinical data, integrating phenotype curation with genomic and proteomic data.
• Apply and support machine learning approaches for prediction, clustering, and risk modeling; experience with LLMs is a plus.
• Collaborate with multi-institutional consortia and contribute to shared deliverables, data harmonization efforts, and consortium-driven analyses.
• Prepare high-quality visualizations, summaries, and reports for manuscripts, grant applications, and presentations.
• Maintain rigorous documentation, version control (Git/GitHub), and reproducibility standards.
• Work in cloud computing environments (e.g. AWS, DNAnexus) and HPC clusters.

Qualifications

• M.S. in Bioinformatics, Biomedical Informatics, Computational Biology, or Genomics. Alternately, M.S. in a discipline requiring strong computational and analytical skills supplemented with some biology exposure.
• Ph.D in a related field preferred. Those with a Bachelors degree and additional post-graduate experience are considered.
• 2+ years post-graduate experience in a research environment, including the manipulation of large biological datasets. 4+ years of experience preferred.
• Advanced knowledge of genetics and/or statistical analysis software and online resources. Experience in programming environments such as MatLab, R statistical package, BioConductor, Perl and C++.
• Preferred: Proficiency in R, Python, and shell scripting. Strong experience with statistical genetics analysis workflows. Hands-on experience analyzing large-scale genomic datasets (e.g., WES/WGS, UK Biobank, All of Us, etc.). Familiarity with proteomics datasets (e.g., Olink, SomaScan) and associated analysis frameworks. Working knowledge of cloud computing environments (e.g. AWS, DNAnexus) and HPC clusters. Experience with APIs, data ingestion/ETL pipelines, and workflow automation (e.g.WDL, Nextflow).