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 FinnGen, 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.

Responsibilities

• 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, FinnGen, 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.

Education

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

Experience

• 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.

Additional Information

• 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.