Job DescriptionWe are seeking a highly motivated
genetic epidemiologist/statistical geneticist to join our growing computational genomics team as Bioinformatician II. The candidate will be working under the supervision of Drs. Ruth Loos and Nathalie Chami. The ideal candidate will bring strong proficiency in R, Python, and Unix/Linux shell scripting, with hands-on experience developing reproducible and scalable analysis pipelines for genomic and multi-omics data.This role will support multiple high-impact research programs involving
biobank-scale datasets, EHR-linked analyses, and multi-omics integration, and will actively contribute to collaborative projects across national and international consortia. Experience with
AI/ML approaches is a strong plus.
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.
- Working knowledge of 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).
• Proficiency with generating and maintaining reproducible pipelines (Git/GitHub).
