Associate Scientist Postdoctoral Fellow - Computational Precision Genetics

Merck • Cambridge, MA

8d • Hybrid

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.
Position summary
The Precision Genetics group in the Data, AI and Genome Sciences Department seeks a Postdoctoral Research Fellow to drive computational work on a translational project developing a reusable multi-omics and AI/ML framework to discover mechanism-based companion diagnostic (CDx) biomarkers that predict treatment response in autoimmune diseases. The role combines organoid drug-profiling, multi-modal functional assays, single-cell and spatial transcriptomics, and development of interpretable AI/ML models to identify predictive biomarkers and benchmark model fidelity against clinical datasets.

Requirements

• Ph.D. or completion within 6 months in Computational Biology, Bioinformatics, Systems Biology, Genomics, Biomedical Engineering, Computer Science (with bioinformatics experience), or related discipline.
• Demonstrated experience analyzing single-cell and/or spatial transcriptomics data processing, clustering, differential expression, spatial analysis).
• Proven ability to apply advanced AI/ML to biomedical data for biomarker discovery/patient stratification, using rigorous evaluation and reproducible Python/R pipelines
• Strong programming skills in Python and/or R and familiarity with relevant libraries/tools (Seurat, Scanpy, Squidpy, Bioconductor, scikit-learn, PyTorch/TensorFlow).
• Strong statistical skills and experience working with high-dimensional biological data; excellent data visualization abilities.
• Excellent written and oral communication skills and evidence of productivity appropriate to career stage (publications, code repositories, or preprints).
• Proven ability to work collaboratively in interdisciplinary teams and manage multiple projects concurrently.

Nice To Haves

• Hands-on experience generating single-cell or spatial transcriptomics datasets from organoid models (10x Visium, Nanostring GeoMx, MERFISH, Stereo-seq) or close collaboration with teams that generate such data.
• Familiarity with genotype/SNP data processing and integration (GWAS summary statistics, imputation, genotype–phenotype association analyses).
• Experience with cloud platforms (AWS) and high-performance computing (HPC) environments.
• Prior experience in translational biomarker discovery or developing clinically oriented predictive models.

Responsibilities

• Analyze multi-modal pre- and post-treatment readouts, including epithelial barrier assays, cytokine profiling, single-cell RNA-seq, and spatial transcriptomics (e.g., 10x Visium, GeoMx, Stereo-seq).
• Develop, benchmark, and maintain reproducible computational pipelines for bulk, single-cell, and spatial transcriptomics data processing (QC, alignment, cell-type annotation, and spatial analyses).
• Implement multi-omic integration strategies combining spatial transcriptomics, single-cell expression, cell composition estimates, and genotype/SNP data.
• Design, train, evaluate, and interpret AI/ML models (supervised and unsupervised) for predictive biomarker discovery and companion diagnostic candidate prioritization, emphasizing feature selection and model explainability.
• Document methods, workflows, and results thoroughly; prepare and contribute to manuscripts, conference presentations, and IP/translation activities as appropriate.
• Collaborate effectively with wet-lab scientists, clinicians, and computational colleagues, present results to the team and stakeholders.

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