Senior Bioinformatics Scientist

About the Role We welcome applications from scientists with a graduate degree (PhD or equivalent) in statistical genetics, genetic epidemiology, computational biology, biostatistics, or a related quantitative field with hands‑on experience analyzing human genetics and multi‑omics data. Our CDG group leverages large‑scale data resources, such as FinnGen, the Alliance for Genomic Discovery, Our Future Health, UK Biobank, Pharma Proteomics Project, Open Targets, and other public and proprietary datasets, to advance the drug development pipeline through human genetics. The candidate will contribute to the analysis of these large‑scale datasets to support target identification and validation and the implementation of precision medicine strategies across therapeutic areas. 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). Assist 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. Required Experience and Skills 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 post‑doctoral 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, Bash). 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. On‑site work is required; remote work is not accepted. 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 RNA‑seq, single‑cell RNA‑seq, spatial transcriptomics, and proteomics (e.g., OLINK). #J-18808-Ljbffr