Associate Director Quantitative Genetics Scientist (Billerica MA)

EMD Serono Research & Development Institute•Billerica, MA

1d•$131,300 - $216,600•Onsite

About The Position

This role is for an Associate Director Quantitative Genetics Scientist at EMD Serono, focusing on leading the human quantitative genetics strategy within neurology and immunology programs. The position involves working with scientists and clinicians across all pipeline stages, from target identification to indication expansion and biomarker selection. The scientist will be responsible for data acquisition strategies, implementing appropriate human genetics methods, performing genetic safety assessments, and providing rapid genetic due diligence for in-licensing opportunities. The role also requires integrating genetic evidence with multi-omic data, supporting pharmacogenomic analyses, establishing standards for genetic evidence, and contributing to portfolio decision-making. Automation of genetics tools, development of interactive dashboards, and collaboration with engineering and data science teams are key aspects. The position emphasizes reproducible research practices and educating R&D scientists on human genetics methodologies.

Requirements

Substantial expertise in genetics with comfort in both strategic questions and hands-on implementation.
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 (GWAS, 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 (e.g., 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.
External applicants 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

Lead human quantitative genetics strategy for neurology and immunology programs across all pipeline stages.
Create strategy for data acquisition and access, identifying relevant public and licensed resources.
Identify and implement appropriate human genetics methods, including new methods from recent literature.
Perform genetic safety assessments for pipeline targets (e.g., loss-of-function carrier phenotyping, phenome-wide association analysis, Mendelian randomization).
Provide rapid genetic due diligence for in-licensing and business development targets.
Integrate genetic evidence with multi-omic data (proteomics, transcriptomics, eQTL/pQTL) for target validation and mechanism-of-action confirmation.
Support pharmacogenomic analyses for dose optimization, PK/PD modeling, and patient stratification.
Create and communicate standards for the strength of human genetic evidence, including green-flag/red-flag frameworks.
Contribute genetic features to probability-of-success models and target prioritization scoring.
Automate genetics tools and reporting for quick turnaround settings and integrate with AI platforms.
Work with engineering teams to develop interactive dashboards and visualizations.
Ensure human genetics tools and reporting are linked to internal decision support frameworks and AI platforms.
Employ best practices from reproducible research for data landscape and code infrastructure.
Collaborate with other data scientists in drug discovery/development program teams.
Educate scientists across the R&D organization on human genetics methods and possibilities.