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. AgileOne