Your Role:
You will lead our human quantitative genetics strategy, working with scientists and clinicians in our neurology and immunology programs at all pipeline stages (target identification, target validation/assessment, safety de-risking, patient selection, indication selection and expansion, biomarker selection, in-licensing due diligence).
To enhance the impact of genetics on our pipeline, you will:
- Create a strategy for data acquisition and access, determining which publicly available and licensed resources (population biobanks, disease-specific cohorts, multi-ethnic datasets) will most effectively support our current and envisioned pipeline.
- Ensure we use the right human genetics methods at the right time. You'll identify the right methods, both the old standards and new methods from recent literature, implementing them in our internal and linked platforms.
- Perform genetic safety assessments for pipeline targets, including loss-of-function carrier phenotyping, phenome-wide association analysis, and Mendelian randomization to anticipate mechanism-based adverse events and on-target toxicity.
- Provide rapid genetic due diligence for in-licensing and business development targets (20-25 per year), assessing target-disease association, genetic safety signals, direction of effect, and competitive positioning.
- Integrate genetic evidence with multi-omic data (proteomics, transcriptomics, eQTL/pQTL) for systems-level target validation and mechanism-of-action confirmation.
- Support pharmacogenomic analyses for dose optimization, PK/PD modeling, and patient stratification strategies to enhance clinical trial design.
- Create and communicate standards for the strength of human genetic evidence, including clear green-flag/red-flag frameworks for target nomination and portfolio decisions.
- Contribute genetic features to probability-of-success models and target prioritization scoring to support portfolio-level decision-making.
- Automate genetics tools and reporting to support both quantitative experts and disease biologists. Focus on quick turnaround settings such as early target screening, in-licensing due diligence, and safety assessment. Integrate with the agentic AI platform for scalable genetic reviews.
- Work with engineering teams to develop interactive dashboards and visualizations to communicate genetic evidence to project teams, leadership, and due diligence committees.
- Ensure that our human genetics tools and reporting are linked to internal decision support frameworks and to internal AI platforms.
- Employ best practices from reproducible research to create a flexible yet FAIR data landscape and high-quality code infrastructure.
- Collaborate with other data scientists in drug discovery/development program teams to ensure that the right questions are asked and answered.
- Educate scientists across the R&D organization on the available methods and possibilities from human genetics.
Who You Are Minimum Qualifications:- You have substantial expertise in genetics and are comfortable working both on strategic questions as well as hands-on implementation.
- You have PhD in statistical genetics, computational biology, human genetics, or a related quantitative field.
- Minimum 6 years relevant experience beyond PhD.
- Demonstrated experience applying computational genetics to complex disease, with relevance to neurology and/or immunology.
- Deep understanding of a wide range of human genetics methods, including GWAS and downstream analyses (e.g., fine-mapping, enrichment, colocalization, eQTL/pQTL mapping, PRSs, Mendelian randomization, direction of effect determination, loss-of-function and gain-of-function analysis) as well as variant interpretation.
- Strong experience with large scale biobank datasets such as the UK Biobank, All of Us, and familiarity with disease-specific and multi-ethnic cohorts.
- Experience with multi-omic data integration (genomics, proteomics, transcriptomics) for target validation and mechanism-of-action studies.
- Experience applying machine learning and AI methods to genomic and multi-omic datasets.
- Strong quantitative and programming skills (e.g., Python and R in Unix/HPC environments, including biobank trusted research environments).
- Proven ability to translate genetic analyses into clear, actionable recommendations for scientific and portfolio decisions, including in-licensing and safety assessments.
Preferred Qualifications: - Experience in pharmaceutical or biotech R&D, particularly in genetics-informed target selection, safety de-risking, or clinical development support.
- Familiarity with pharmacogenomics and its application to dose optimization and PK/PD modeling.
- Experience building automated analysis pipelines, dashboards, or agentic AI workflows for genetic evidence generation.
- Track record of working with cross-functional teams including biologists, clinicians, and business development professionals.
Location: On-site Billerica MA
Pay Range for this position: $131,300 - $216,600
The offer range represents the anticipated low and high end of the base pay compensation for this position. The actual compensation offered will be determined by factors such as location, level of experience, education, skills, and other job-related factors. Position may be eligible for sales or performance-based bonuses. Benefits offered by the Company include health insurance, paid time off (PTO), retirement contributions, and other perquisites. For more information click
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