Senior Data Scientist, Statistical Genetics

A pioneering life sciences research organization is looking for a Data Scientist or Senior Data Scientist specializing in statistical genetics and computational biology. The mission centers on leveraging advanced computational approaches and in vivo model systems to deepen insights into the biological and genetic mechanisms controlling human aging. The work enables innovative interventions that support longer, healthier lives. The environment offers a strong emphasis on fundamental discovery science, integration of academic and industry partnerships, and a dynamic therapeutic R&D pipeline.

Key Responsibilities:

• Apply and advance statistical and computational methods for interpreting biobank-scale and large cohort datasets, encompassing high-dimensional phenotypes and longitudinal health data.
  

• Design and lead investigations to understand the genetic architecture of age-related diseases and trait trajectories across populations.
  

• Merge multiple biological datasets (e.g., clinical, genetic, multi-omics) to build new hypotheses for novel treatments targeting aging-associated conditions.
  

• Develop reusable software tools and workflows streamlining analysis and data processing in diverse cohort research projects.
  

• Communicate results and collaborate closely with interdisciplinary teams spanning multiple scientific domains—internally and with external research partners.
  

Requirements

• Ph.D. or equivalent advanced training in genetics, statistical genetics, computational biology, bioinformatics, or a related discipline.
  

• Demonstrated expertise in creating and deploying computational or statistical methodologies for large, complex biological/phenotypic datasets.
  

• Proficiency with modern genetic analysis tools—including GWAS, burden tests, fine-mapping, LD score regression, QTL mapping (eQTL/pQTL), colocalization, polygenic risk scoring, Mendelian randomization—and specific techniques for studies involving diverse ancestry groups.
  

• Hands-on experience analyzing large-scale clinical and molecular data, such as genomics, imaging, multi-omics, and longitudinal datasets.
  

• Knowledge of or experience with substantial human cohort research (e.g., UK Biobank, FinnGen, All of Us, or similar repositories).
  

• Advanced coding skills in Python and/or R, with a portfolio of developed tools, libraries, or pipelines accessible to other scientists.
  

• Excellent communication and teamwork abilities, with a strong history of collaborating across varied scientific specialties.
  

• Onsite work required a minimum of 4 days per week.