Associate Scientist, Postdoctoral Fellow - Integrative Multi-Omics Causal Modeling (Genetics, Genomics)

MSD • Cambridge, MA

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About The Position

Be a part of the legacy: Postdoctoral Research Fellow Program Our Research Laboratories’ Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery. Postdoctoral Researcher- Integrative Multi-Omics Causal Modeling (Genetics, Genomics) We are seeking a highly motivated and innovative postdoctoral researcher to join the Data, AI and Genome Sciences (DAGS) department to develop and apply cutting‑edge computational and statistical methods for drug target discovery through multi‑omics integration. The project will develop genetics‑informed causal modeling frameworks by integrating human genetics and genomics data with perturbation‑based sequencing datasets. This unique collaborative role between the Precision Genetics and Genome Sciences groups within DAGS aims to uncover causal gene–pathway–disease relationships, illuminate disease mechanisms, and advance translational research across multiple therapeutic areas.

Requirements

• Candidates must currently hold a Ph.D or Ph.D. completed within 6 months of hire in Statistics, Biostatistics, Computer Science, Mathematics, Statistical Genetics, Computational Biology, Bioinformatics, or a related quantitative field.
• Demonstrated experiences analyzing large-scale genomics datasets, such as RNA seq, single cell RNA seq, proteomics etc.
• Hands‑on experience applying and/or developing statistical genetics methods such as GWAS, QTL mapping, variant‑set association tests, polygenic scores, and heritability estimation etc.
• Proficient programming skills in R and/or Python for data analysis, statistical modeling, and pipeline development.
• Familiarity with cloud platforms or HPC environments, with experiences on parallel computing and scalable workflow design.
• Familiarity with causal inference methods, network/graphical models, or machine‑learning approaches applied to genomics.
• Ability to work independently and collaboratively in a multidisciplinary team environment.
• Excellent written and oral communication skills, with a demonstrated ability to publish methodological papers or innovative applications in statistical genetics or computational biology.

Nice To Haves

• Prior experience with perturbation sequencing assays (CRISPR screens, Perturb‑seq, Drug‑seq) and analysis of perturbation data.
• Experiences in integrating multiple omics data types (e.g., genetics, single cell RNA seq etc.)
• Experience with Bayesian modeling, graphical models, or causal discovery algorithms.
• Experience working with large‑scale biobank (UK Biobank etc.) or consortium datasets and secure research environments (TREs, DNAnexus etc.).

Responsibilities

• Develop scalable and robust statistical and computational methods for causal modeling by integrating human genetics, genomics and perturbation datasets.
• Build computational pipelines for genetic association analyses, cell-disease interaction modeling, perturbation-informed pathway and network inference etc.
• Apply causal inference and network approaches to infer gene to pathway to phenotype relationships to uncover causal molecular mechanisms.
• Continuously track cutting‑edge computational and statistical methods in statistical genetics and computational biology, and proactively propose and pilot innovative ideas and approaches.
• Collaborate closely with wet-lab scientists and cross-functional computational teams.
• Communicate findings effectively through publications, presentations, and collaborative meetings.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees