Associate Director Quantitative Genetics Scientist (Billerica MA)

Merck•Billerica, MA

2d•Onsite

About The Position

This role is for an Associate Director Quantitative Genetics Scientist at EMD Serono in Billerica, MA. The position involves leading the human quantitative genetics strategy for neurology and immunology programs across all pipeline stages. The scientist will be responsible for data acquisition, ensuring the correct application of genetics methods, performing genetic safety assessments, providing genetic due diligence for in-licensing opportunities, integrating genetic evidence with multi-omic data, and supporting pharmacogenomic analyses. The role also includes creating standards for genetic evidence, contributing to probability-of-success models, automating genetics tools, and collaborating with engineering teams to develop interactive dashboards. The scientist will work with AI platforms and ensure reproducible research practices. Collaboration with other data scientists and educating R&D scientists on human genetics methods are also key aspects of the role.

Requirements

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.
Must be eligible to work in the US. This role does not offer sponsorship for work authorization.

Nice To Haves

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.

Responsibilities

Create a strategy for data acquisition and access, determining which publicly available and licensed resources will most effectively support the current and envisioned pipeline.
Ensure the right human genetics methods are used at the right time, identifying and implementing both standard and new methods.
Perform genetic safety assessments for pipeline targets, including loss-of-function carrier phenotyping, phenome-wide association analysis, and Mendelian randomization.
Provide rapid genetic due diligence for in-licensing and business development targets, 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.
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.
Automate genetics tools and reporting to support both quantitative experts and disease biologists, focusing on quick turnaround settings.
Integrate with the agentic AI platform for scalable genetic reviews.
Work with engineering teams to develop interactive dashboards and visualizations to communicate genetic evidence.
Ensure that human genetics tools and reporting are linked to internal decision support frameworks and 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.
Educate scientists across the R&D organization on the available methods and possibilities from human genetics.