Sr. Bioinformatics Scientist

The Complex Disease Genetics (CDG) group within Merck’s Data, AI and Genome Sciences (DAGS) Department is seeking a motivated scientist to support our Cambridge-based research initiatives. The CDG group leverages large-scale, cutting-edge data resources—such as Finn Gen, the Alliance for Genomic Discovery, Our Future Health, UK Biobank Pharma Proteomics Project, and Open Targets—to advance Merck’s drug development pipeline through human genetics.

In this exciting role, you will analyze large-scale datasets and integrate multi-omics data to support target identification, target validation, and the implementation of precision medicine strategies across multiple therapeutic areas.

• Perform statistical genetics analyses for target discovery and validation using human genetics and multi-omics data.
• Conduct genetic association analyses and meta-analyses using public, proprietary, and large-scale biobank data (e.g., UK Biobank, Finn Gen, Our Future Health, Alliance for Genomic Discovery).
• Support the development, implementation, and maintenance of analytical pipelines to ensure reproducible and scalable genetic and genomic data analysis.
• Perform advanced post-GWAS analyses to help elucidate causal mechanisms and prioritize gene targets (including fine mapping, colocalization, Mendelian Randomization, TWAS, and polygenic risk prediction).
• Assist in integrating genetic association findings with multi-omics data (e.g., RNA-seq, ATAC-seq, QTLs) to further support target prioritization.
• Stay current with new methodologies in statistical genetics, actively participating in the evaluation and implementation of emerging analytical techniques.
• Collaborate cross-functional with wet-lab biologists, disease area experts, and data scientists to support ongoing research and patient stratification strategies.

• PhD (or equivalent graduate degree) in statistical genetics, genetic epidemiology, population genetics, computational biology, bioinformatics, biostatistics, epidemiology, or a related quantitative discipline.

• Minimum of 5 years of postdoctoral or equivalent research experience in complex disease genetics.
• Proven research experience in human genetics, genomics, or related analysis, including genome-wide association studies (GWAS) and/or multi-omics analysis.
• Proficiency in programming languages commonly used in statistical genetics (e.g., R, Python) alongside familiarity with analytical pipelines and best practices for reproducibility.
• Demonstrated experience working with large-scale datasets in cloud-based computing and high-performance computing (HPC) environments.
• Strong communication and interpersonal skills, with a track record of working effectively in multidisciplinary teams.

• Location:
  
  Cambridge-based research initiatives.
• Preferred Experience:
  
  Hands-on experience working with molecular phenotypes, such as transcriptomics or proteomics.
• Experience with AI/ML methodology and/or its direct application to genetics and omics analysis.
• A professional interest or background in complex diseases such as cardiovascular, metabolic, immunology, or neuroscience.