Sr. Bioinformatics Scientist

The Precision Genetics group within the Data and Genome Sciences Department is seeking a skilled Contractor to join our Computational Precision Genetics team in Cambridge, MA. In this role, you will be a key contributor combining deep expertise in human genetic data analysis with strong machine learning capabilities and hands-on multi-omics integration. Working in a dynamic environment, you will support target identification, patient stratification, and biomarker discovery efforts to advance innovative therapeutic pipeline targets.

Responsibilities

• Query and harmonize external genomic and multi-omics resources (e.g., dbSNP, 1000 Genomes, gnomAD, GTEx, Open Targets, ClinVar, GWAS Catalog, UK Biobank) to acquire key datasets.
• Perform quality control, genotype imputation, and variant calling/annotation using standard tools such as GATK, IMPUTE, Minimac, ANNOVAR, and VEP.
• Conduct large-scale genetic association analyses, including GWAS/PheWAS, rare-variant burden tests, fine-mapping, colocalization, polygenic scores, and Mendelian randomization using PLINK, REGENIE, SuSiE, and LDSC.
• Perform eQTL, sQTL, and pQTL mapping using tools such as tensorQTL, FastQTL, or R/qtl to link genetic loci with quantitative molecular traits.
• Analyze population structure, linkage disequilibrium, relatedness, and allele frequency distributions across diverse cohorts.
• Develop, benchmark, and validate machine learning models (using scikit-learn, PyTorch, XGBoost, and SHAP) for variant effect prediction, patient stratification, and biomarker discovery while controlling for confounding factors.
• Integrate genetic data with bulk/single-cell transcriptomics, proteomics (e.g., OLINK, mass spectrometry), and epigenomics to reveal disease biology mechanisms using DESeq2, Seurat, and scanpy.
• Maintain detailed, version-controlled documentation and build reproducible data analysis workflows using Git, containerization tools, and workflow managers.

Education

• Ph.D. in Genetics, Genomics, Statistical Genetics, Computational Biology, or a related quantitative field (candidates with only a BS or MS will not be considered).

Experience

• 5+ years of hands-on experience in human genetic data analysis and statistical genetics methods.
• Proven track record applying machine learning algorithms to high-dimensional biological datasets, emphasizing robust cross-validation and feature selection.
• Practical experience integrating multi-omics datasets (transcriptomics, proteomics, epigenomics) alongside genetic data.
• Proficiency in R, Python, and Bash within High-Performance Computing (HPC) environments and AWS Cloud infrastructure (e.g., S3, IAM).
• Preferred: Experience using biobank-scale datasets (UK Biobank, FinnGen, All of Us), deep learning models for sequence/variant effect prediction, single-cell/spatial transcriptomics, workflow managers (Nextflow, Snakemake), and supporting target discovery in a biotech or pharma environment.

Additional Information

• Location: Onsite in Cambridge, MA. Remote work arrangements are not available for this contract position.
• Work Style: Requires a collaborative, self-motivated individual with excellent written and verbal communication skills who can manage multiple objectives in a fast-paced environment.