Bioinformatics Scientist - III (Senior)

Responsibilities

• Perform statistical genetics analyses for target discovery and validation using human genetics and multi-omics data
• Support the development, implementation, and maintenance of analytical pipelines for reproducible genetic and genomic data analysis
• Conduct genetic association analyses using large-scale biobank data (e.g., UK Biobank, FinnGen, Our Future Health, Alliance for Genomic Discovery)
• Integrate and analyze public and proprietary genetic association summary statistics and conduct meta-analyses
• Perform post-GWAS analyses to help elucidate causal mechanisms and prioritize gene targets (e.g., fine mapping, colocalization, Mendelian Randomization, TWAS, polygenic risk prediction)
• Auxiliary in integrating genetic association findings with multi-omics data (e.g., RNA-seq, ATAC-seq, QTLs) to support target prioritization
• Stay current with new methods in statistical genetics and participate in evaluating and implementing emerging analytical techniques
• Collaborate with wet-lab biologists, disease area experts, and data scientists to support research and patient stratification strategies

Requirements

• PhD (or equivalent) in statistical genetics, genetic epidemiology, population genetics, computational biology, bioinformatics, biostatistics, epidemiology, or a related quantitative discipline, with a minimum of 5 years of postdoctoral or equivalent research experience in complex disease genetics
• Research experience in human genetics, genomics, or related analysis, including genome-wide association studies (GWAS) and/or multi-omics analysis
• Familiarity with analytical pipelines and best practices for reproducibility and scalability in genetic data analysis
• Proficiency in programming languages commonly used in statistical genetics (e.g., R, Python, etc.)
• Experience working with large-scale datasets in cloud-based computing and high-performance computing environments
• Strong communication and interpersonal skills, with the ability to work effectively in multidisciplinary teams

Preferred Experience and Skills

• Experience with molecular phenotypes, such as transcriptomics or proteomics
• Interest or background in cardiovascular/metabolic diseases, immunology, neuroscience, or other complex diseases
• Experience with AI/ML methodology and/or application to genetics and omics analysis
• Experience with multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq, transcriptomics analysis)
• Proficiency in R and Bash
• Experience with high-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets)