Sr. Scientist, Bioinformatics, Statistical Genetics

Job Description

The Precision Genetics group within the Data AI and Genome Sciences Department is seeking a Senior Scientist to join our Statistical Genetics team in Cambridge, MA. We are looking for a skilled statistical geneticist with experience in whole genome sequence analysis, large scale biobank data and post-GWAS analysis to identify novel therapeutic targets and support existing targets

Key Responsibilities:

• Statistical Genetic Analysis: Deploy statistical genetics analysis approaches on large-scale individual level genetic data (sequence and microarray) from biobanks to identify and support therapeutic targets and identify novel patient subpopulations.

• Phenotype Curation: Curate electronic health records (EHRs) to create novel phenotypes for analysis

• Data Collection: Proactively identify relevant genetic datasets from public, internal, and proprietary sources through collaborative efforts

• Collaborative Research: Partner closely with AI/ML scientists and disease area biologists to develop tailored analytical strategies for complex diseases across various therapeutic areas.

• Strategic Partnership: Serve as a strategic collaborator with discovery teams to influence the drug discovery portfolio.

• Hands-On Analysis: Implement rigorous data analysis practices and remain informed about cutting-edge statistical genetics approaches, particularly the implementation of AI/ML or omics integration.

• Post-GWAS analysis: Perform post-GWAS analysis to generate target hypothesis and support

• Omics Integration: Integrate genetic analysis with transcriptomics and proteomics data, including colocalization and QTL approaches

• Emerging Methods: Stay at the forefront of novel methodologies in statistical genetics.

• Project Management: Manage complex projects, proactively identifying challenges and forecasting timelines for key deliverables to meet pipeline objectives.

• Validation Collaboration: Work with wet-lab biologists and disease area experts to validate targets and identify patient sub-populations.

• Effective Communication: Present findings to project teams, internal stakeholders, and the broader scientific community through internal documentation, presentations, and publications in leading journals.

Required Qualifications:

• Ph.D. in Statistical Genetics, Human Genetics or a related field, with a minimum of 2 years of post-PhD research experience.

• Proven track record of over 2 years of applying statistical genetics approaches to large-scale population genetics resources for target discovery.

• Fundamental knowledge of statistical and population genetics principles.

• Strong conceptual understanding of statistical genetics analytic methods, including Mendelian randomization and fine-mapping/colocalization

• Strong conceptual understanding of common statistical techniques, including regression, dimensionality reduction, network analysis, and data harmonization.

• Proficiency in coding using R and Python, with the ability to establish best practices for reproducible data analyses.

• Expertise in using common statistical genetics software, including PLINK, GATK, and REGENIE

• Hands-on experience working with biobank data, including biobank trusted research environments (TREs)

• Experience working with AWS or other cloud-based computational clusters

• Excellent written and verbal communication skills.

Preferred Qualifications:

• Experience in fields such as Neuroscience, Cardiovascular and Metabolic diseases, Immunology, or other complex diseases.

• Familiarity with integrating transcriptomics and proteomics data with genomics data

• Experience analyzing longitudinal data

• Conceptual understanding of AI/ML approaches, including foundation modeling

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