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Merck · 12 hours ago

Associate Scientist Postdoctoral Fellow - Computational Precision Genetics

Cambridge, MA

Full-time

Hybrid

New Grad, Entry Level

Merck is a leading pharmaceutical company seeking a Postdoctoral Research Fellow in the Precision Genetics group. The role focuses on developing a reusable multi-omics and AI/ML framework for discovering biomarkers in autoimmune diseases through computational analysis and collaboration with various scientific teams.

BiotechnologyHealth CareMedicalPharmaceutical

Comp. & Benefits

H1B Sponsored

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

Qualification

Computational BiologyAI/ML ModelsSingle-Cell GenomicsPython/R ProgrammingBioinformaticsStatistical SkillsScientific WritingData VisualizationLarge Scale Data ProcessingCollaboration Skills

Required

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

Preferred

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