Postdoc: Integrative Genomics & Multi-Omics Data Science

MSD Malaysia; Cambridge (MA); On-site; USD 82,000 - 92,000; Full time

Job summary

MSD Malaysia invites a highly motivated postdoctoral researcher to join the Precision Genetics group in the Data, AI and Genome Sciences department. You will develop scalable computational pipelines to integrate genetics, proteomics, EHR and other big datasets for biomarker discovery and causal modeling, collaborating across teams and publishing findings.

The role emphasizes applying GWAS, polygenic scores, and advanced statistical genetics to uncover gene–pathway–disease relationships, with

Qualifications

• Experience with large-scale biobank or consortium datasets.
• Experience analyzing omics datasets (WGS, proteomics, transcriptomics).
• Proficient in statistical genetics methods (GWAS, polygenic scores).
• Proficient in R and/or Python for data analysis and modeling.
• Experience with cloud/HPC and scalable workflows.
• Strong publication record in statistical genetics or computational biology.

Responsibilities

• Develop scalable pipelines for integrating genetics, proteomics, EHR, and other datasets for target and biomarker discovery.
• Develop causal modeling methods by integrating genetics, genomics and perturbation datasets.
• Analyze biobank-scale data and interpret associations and biomarker signals.
• Keep up with cutting-edge statistical genetics methods and propose innovative ideas.
• Collaborate with wet-lab and cross-functional computational teams.
• Communicate findings through publications and presentations.

Skills

Bioinformatics

Biomarker Discovery

Computational Biology

Genetic Analysis

Genetic Epidemiology

Genomics

Human Genetics

Omics

Proteomics

Statistical Genetics

Transcriptomics

Education

PhD required in quantitative field

Tools

AWS

Job description

MSD Malaysia invites a highly motivated postdoctoral researcher to join the Precision Genetics group in the Data, AI and Genome Sciences department. You will develop scalable computational pipelines to integrate genetics, proteomics, EHR and other big datasets for biomarker discovery and causal modeling, collaborating across teams and publishing findings.

The role emphasizes applying GWAS, polygenic scores, and advanced statistical genetics to uncover gene–pathway–disease relationships, with