Senior Scientist, Genomic Biomarkers & Companion Diagnostics Full-time NEW

Senior Scientist

The Precision Genetics group within the Data, AI and Genome Sciences Department is seeking a Senior Scientist to join our Computational Precision Immunology team in Cambridge, MA. We are looking for a skilled statistical geneticist with extensive experience to develop genetic and genomic biomarkers with companion diagnostic (CDx) potential in immunology, leveraging large-scale genetic datasets and state-of-the-art analytical methods.

Key Responsibilities

• Genetic Biomarker Discovery: Identify and validate predictive genetic variants, polygenic risk scores (PRS), and pharmacogenomic (PGx) signatures with CDx potential in immunological diseases, including autoimmune conditions.
• Statistical Genetics & Genomic Analyses: Design and execute a broad range of statistical genetics analyses to uncover disease-associated loci, characterize genetic architecture, and support biomarker development – including genome-wide association studies (GWAS) and population genetics (e.g., PLINK2, REGENIE), rare variant association analyses and gene burden testing (e.g., SAIGE-GENE, SKAT), polygenic risk score (PRS) development and evaluation for patient stratification (e.g., PRSice-2, LDpred2, PRS-CS), and pharmacogenomics (PGx) investigations into genetic determinants of drug response and toxicity.
• Genetic Data Processing & QC: Process and perform rigorous quality control of diverse genetic data types including WGS, WES, SNP arrays, and targeted sequencing panels (e.g., GATK, bcftools, PLINK2).
• Variant Annotation & Interpretation: Annotate and prioritize genetic variants for functional and clinical significance using established tools and databases (e.g., VEP, ANNOVAR, ClinVar, gnomAD, dbSNP, CADD).
• Multi-Omics Integration: Integrate genetic data with complementary molecular data types (e.g., gene expression, proteomics, splicing) to build mechanistic understanding and strengthen biomarker evidence (e.g., eQTL, colocalization analyses using coloc, SMR).
• Data Ingestion: Query and acquire relevant datasets from external genetic and genomic databases (e.g., UK Biobank, gnomAD, GTEx, ClinVar, dbGaP, GWAS Catalog).
• Documentation: Prepare clear, detailed documentation of analytical methods, results, and findings in a timely and reproducible manner to support internal decision-making and potential regulatory submissions.

Required Qualifications

• Ph.D. in Statistical Genetics, Computational Biology, Human Genetics, Bioinformatics, or a related field.
• A proven track record of 5+ years of hands-on experience in statistical genetics and genomic data analysis.
• Deep expertise across GWAS, population genetics, rare variant analysis, PRS development, and pharmacogenomics.
• Strong proficiency in processing and analyzing diverse genetic data types: WGS, WES, SNP arrays, and targeted sequencing panels.
• Solid command of statistical methods relevant to human genetics, including mixed models, linkage disequilibrium (LD) analysis, fine-mapping, and genetic confounding correction.
• Proficiency in R, Python, and Bash, with the ability to build reproducible, well-documented analytical pipelines.
• Experience with high-performance computing (HPC) systems and cloud platforms (e.g., AWS S3, IAM).
• A collaborative and self-motivated individual with a strong work ethic, capable of managing multiple objectives in a dynamic environment and adapting to changing priorities.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Understanding of immunology and/or autoimmune disease biology (e.g., mechanisms of immune dysregulation, relevant disease areas such as rheumatoid arthritis, lupus, IBD).
• Experience with multi-omics data analysis and integration (e.g., RNA-seq, single-cell RNA-seq, proteomics, spatial transcriptomics).
• Familiarity with eQTL analysis, Mendelian randomization, and colocalization methods to link genetic signals to molecular mechanisms.
• Experience with real-world data (RWD) or large-scale biobank datasets (e.g., UK Biobank, FinnGen, All of Us).
• Awareness of regulatory frameworks for companion diagnostics, including FDA CDx guidance and IVD regulations.
• Familiarity with fine-mapping tools (e.g., FINEMAP, SuSiE) and functional genomics annotation resources (e.g., ENCODE, Roadmap Epigenomics).
• Experience with ancestry-diverse cohort analyses and methods for handling population stratification.