Senior Computational Geneticist

ROLE SUMMARY 

Human genetics has established itself as a cornerstone of modern drug discovery, yet we are only beginning to unlock the full potential of the rich biomedical data now being generated.

We are seeking a geneticist with a passion for applying cutting-edge approaches, including genetic epidemiology, quantitative genetics, computational biology, AI, functional omics, epigenetics, and other multi-omics data resources, to accelerate the identification of causal mechanisms, therapeutic indications, biomarkers, and patient stratification strategies.

The Integrative Biology team within the Internal Medicine Research Unit works closely with disease area biologists to address unmet medical needs in metabolic diseases, including obesity and cardiovascular disease, with a particular focus on atherosclerotic cardiovascular disease. We do this by developing and applying advanced methods to analyze human genetics and other large-scale molecular datasets.

In addition, we collaborate with AI and machine learning teams to develop methods and tools that enhance our ability to work with and interpret complex genetics and genomics data. These tools help deliver meaningful insights and actionable recommendations to disease area project teams.

The ideal candidate for this role will also work with our external partners and collaborators to help manage external collaborations, ensure the timely delivery of high-quality genetics and genomics data, and to incorporate results into target identification and validation. 

The applied human genetics scientist position offers an opportunity to execute science-based drug discovery within one of the world’s leading developers of human therapeutics, at Pfizer’s Kendall Square research facility in Cambridge Massachusetts. 

ROLE RESPONSIBILITIES  

• Provide quantitative genetics expertise and conduct analyses to derive impactful results through interactions with biologists within the Internal Medicine Research Unit. 

• Provide expertise and quantitative skills on the application of genetics and functional genomics to inform project teams in: 
  - Identification of novel therapeutic targets 
  - Review and validation of therapeutic hypotheses 
  - Mechanistic understanding of disease pathogenesis and causal pathways 
  - Innovative approaches to identifying mechanism related biomarkers via integrating genetics with clinical and ‘omic datasets. 
  - Identification of potential disease indications. 
  - Matching novel therapies to patients with relevant disease sub-types. 

• Ensure high-quality genetic and ‘omics data is incorporated into exploratory research by interacting with internal partners in statistics, bioinformatics, computational biology, clinicians, and project leaders to drive genetic data analysis, data integration, and genetic methodology. 

• Work with our partners in machine learning and AI to build tools that provide genetics / genomics results to our scientists and other stakeholders. 

• Work in collaboration with research biologists and project teams to identify the best opportunities to translate genetic and related data to inform and prioritize assets in the portfolio; and with clinical teams to inform optimum patient selection, stratification and trial design. 

• Manage, develop and maintain internal and external collaborative projects to specified timelines and milestones 

• Communicate study findings and analyses with internal partners, leadership, administration, and collaborators. 

• Manage, support and promote relationships with the external community that can add value to target selection and validation at Pfizer 
• Willingness to learn new skills and be “change agile” 

BASIC QUALIFICATIONS  

• Background in biological and/or quantitative sciences; PhD required and post-doctoral experience in genetics, statistical genetics, or genetic epidemiology with two (2) years relevant experience 
• Sound statistical and quantitative skills, with knowledge of epidemiological principals and population-based research. 
• Experience managing large projects, collaborations, or consortia 
• Use of generative AI tools such as Microsoft copilot, github copilot, ChatGPT, Claude, Gemini, etc.
• Experience analyzing genetic data sets andintegrating additional data types to further clinical and biological understanding 
• Deep and abiding curiosity about biological and pathological processes and mechanisms. 
• Proficiency in programming, scripting, querying or statistical analysis languages such as R, python, perl, SQL. 
• Familiarity with genetics platforms
• Able to effectively interact and communicate with multidisciplinary scientists, researchers, and non-sc