Senior Scientist, Genomic Biomarkers & Companion Diagnostics

About this role

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.
• #EligibleforERP
• Required Skills:
• Bash (Programming Language), Computational Biology, Data Science, Gene Expression, Genome-Wide Association Study (GWAS), Genomic Data Analysis, Genomics, High Performance Computing (HPC), Machine Learning (ML), Pharmacogenomics, Population Genetics, Python (Programming Language), RNA Sequencing, R-Studio, Statistical Genetics
• Preferred Skills:
• Autoimmune Diseases, Immunology, Omics, Real World Data
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• HERE
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• HERE
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• if you need an accommodation during the application or hiring process.
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• characteristics.  As
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• U.S. Hybrid Work Model
• Effective September 5, 2023, employees in office-based positions in the U.S. will be working a Hybrid work consisting of three total days on-site per week, Monday - Thursday, although the specific days may vary by site or organization, with Friday designated as a remote-working day, unless business critical tasks require an on-site presence.This Hybrid work model does not apply to, and daily in-person attendance is required for, field-based positions; facility-based, manufacturing-based, or research-based positions where the work to be performed is located at a Company site; positions covered by a
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• agreement (unless the agreement provides for hybrid work); or any other position for which the Company has determined the job requirements cannot be reasonably met working remotely. Please note, this Hybrid work model guidance also does not apply to roles that have been designated as “remote”.