About the position
The Complex Disease Genetics group within the Data, AI and Genome Sciences (DAGS) Department at our Company is seeking an accomplished Associate Director /Associate Principal Data Scientist to advance our Cambridge-based research initiatives in complex disease genetics. We invite applications from highly motivated scientists with a doctoral degree (PhD or equivalent) in statistical genetics, genetic epidemiology, computational biology, biostatistics, or a related discipline. The ideal candidate will demonstrate a robust track record in research and publication, expertise in large-scale human genetics and multi-omics analyses, and a passion for translational research in drug discovery and precision medicine. As a member of the Precision Disease Genetics group, you will leverage advanced statistical genetics methodologies to analyze large-scale public and proprietary datasets, including population-based biobanks, deeply phenotyped patient cohorts, and family-based studies, which encompass genetic and genomic data linked with electronic health records, clinical, biomarker, imaging, and clinical laboratory data. Our Company has strategically invested in external partnerships and collaborative initiatives to accelerate innovation in drug discovery and development. The Complex Disease Genetics group is aiming to leverage these world-class resources, including FinnGen, the Alliance for Genomic Discovery, Our Future Health, UK Biobank Pharma Proteomics Project, Open Targets, and a range of other public and proprietary datasets, to advance our Company's drug development pipeline through cutting-edge human genetics research. Your research will directly inform our Company's drug development pipeline, supporting target identification and validation and the implementation of precision medicine strategies across therapeutic areas. You will have the opportunity to lead and shape collaborative research efforts, both internally and with external academic and industry partners, to drive innovation and maximize the translational impact of human genetics on drug development.
Responsibilities
- Execute advanced statistical genetics analyses for target discovery, validation, and precision medicine using human genetics, multi-omics, and real-world data to deliver reproducible results
- Design, implement, and optimize standardized analytical pipelines that can be updated and maintained over time and enable rapid discovery and replication cycles
- Conduct common and rare genetic association analyses using large-scale population-based biobank data (e.g., UK Biobank, FinnGen, Our Future Health, Alliance for Genomic Discovery, etc.)
- Utilize and integrate public summary statistics and perform meta-analyses to maximize statistical power
- Perform post-GWAS analyses to elucidate causal mechanisms and prioritize gene targets (e.g., fine mapping, colocalization, Mendelian Randomization, TWAS, heritability and genetic correlation estimation, pathway and functional enrichment analysis, polygenic risk prediction, etc.)
- Integrate genetic association findings with multi-omics data (e.g., RNA-seq, ATAC-seq, ChIP-seq, QTLs, etc.) to construct multi-layered evidence for target prioritization and mechanistic understanding
- Stay at the forefront of methodological innovation in statistical genetics, evaluating and implementing emerging analytical techniques
- Drive cross-functional projects and ensure timely delivery of high-impact scientific results to support pipelines
- Collaborate closely with wet-lab biologists, disease area experts, and data scientists to evaluate therapeutic hypotheses and support patient stratification strategies
- Effectively communicate scientific findings to internal project teams, the broader scientific community at our Company, and externally through high-impact publications and presentations at conferences
Requirements
- PhD (or equivalent) in statistical genetics, genetic epidemiology, computational biology, biostatistics, or a related quantitative discipline, with a minimum of 3 years of postdoctoral or equivalent research experience in complex disease genetics
- Will consider candidates with either genetics/genomics background or real-world data background
- Demonstrated expertise and working experience in large-scale statistical genetics and genomics analyses, including genome-wide association studies (GWAS), rare variant association analyses, post-GWAS and multi-omics analysis (e.g., fine mapping, colocalization, Mendelian Randomization, TWAS, etc.), and polygenic prediction
- Ability to implement and apply analytical pipelines for the large-scale human genetic and multi-omics datasets, following best practices for reproducibility and scalability
- Proficiency in programming languages commonly used in statistical genetics research (e.g., R, Python, etc.)
- Experience working with cloud-based computing environments and high-performance computing clusters
- Expertise in working with complex phenotype and/or real-world data, such as electronic health records (EHRs), image data, digital biomarkers, etc.
- Expertise in working with molecular phenotypes, such as transcriptomics (bulk, single-cell, spatial), proteomics, etc.
- Strong record of scientific achievement, such as publications in high-impact, peer-reviewed journals and presentations at leading scientific conferences
- Familiarity with the pharmaceutical drug discovery and development process, and application of statistical genetics and genomics in drug development
- Outstanding communication and interpersonal skills, with a demonstrated ability to collaborate effectively within multidisciplinary teams, including statistical geneticists, biologists, clinicians, and data scientists
Nice-to-haves
- Subject-matter knowledge in neuroscience, cardiovascular and metabolic diseases, immunology or other complex diseases
Benefits
- Medical, dental, vision healthcare and other insurance benefits (for employee and family)
- Retirement benefits, including 401(k)
- Paid holidays, vacation, and compassionate and sick days
