Senior Scientist - Quantitative Genetics

Merck•Billerica, MA

3d•Onsite

About The Position

Work Your Magic with us! Start your next chapter and join EMD Serono. Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet. That's why we are always looking for curious minds that see themselves imagining the unimaginable with us. United As One for Patients, our purpose in Healthcare is to help create, improve and prolong lives. We develop medicines, intelligent devices and innovative technologies in therapeutic areas such as Oncology, Neurology and Fertility. Our teams work together across 6 continents with passion and relentless curiosity in order to help patients at every stage of life. Joining our Healthcare team is becoming part of a diverse, inclusive and flexible working culture, presenting great opportunities for personal development and career advancement across the globe. This role does not offer sponsorship for work authorization. External applicants must be eligible to work in the US. EMD Serono in Billerica, MA, is adding a Senior Scientist - Quantitative Genetics to their team. In this role you will employ human quantitative genetics to advance 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) in our neurology and immunology portfolio. You will work with other quantitative scientists as well as biologists, disease experts, and clinicians to deliver results that support program decision making.

Requirements

PhD in Statistical Genetics, Computational Biology, Human Genetics, or other quantitative research discipline
2+ years of experience post PhD in pharma, biotech, or academic research setting
2+ years of experience in quantitative/computational genetics including hands-on implementation across a range of methods and technologies
2+ years of experience with 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

Nice To Haves

Demonstrated experience applying computational genetics to complex disease, with relevance to neurology and/or immunology
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
Experience with large scale biobank datasets such as the UK Biobank and All of Us as well as familiarity with disease-specific and multi-ethnic cohorts
Experience applying machine learning and AI methods to genomic and multi-omic datasets.

Responsibilities

Collaborate with other data scientists in drug discovery/development program teams to ensure that the right questions are asked and answered.
Play a key role in performing analyses and explaining the results to other scientists.
Ensure we use the right human genetics methods at the right time.
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.
Automate genetics tools and reporting to support both quantitative experts and disease biologists.
Focus on quick turnaround settings such as early target screening, search/in-licensing, and safety assessment.
Integration with our 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.
Educate scientists across the R&D organization on the available methods and possibilities from human genetics.