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Merck Sharp & Dohme Corp is seeking a Director of Data Science to lead its Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics department. The ideal candidate will have extensive experience in the pharmaceutical industry, a strong background in AI/ML, and excellent leadership skills to drive innovative data science strategies.
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Job Summary
This role is responsible for defining and executing the comprehensive data science strategy for the PDMB department while leading a world-class team.
The successful candidate will deploy cutting-edge AI innovations including Generative AI, Large Language Models, and Graph Neural Networks to enhance R&D predictivity.
Employees are eligible for an annual bonus, long-term incentives, and a comprehensive benefits package including medical, dental, vision, and 401(k) retirement plans.
Matching Summary
Match Score: 75
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Merck Sharp & Dohme Corp is seeking a Director of Data Science to lead its Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics department. The ideal candidate will have extensive experience in the pharmaceutical industry, a strong background in AI/ML, and excellent leadership skills to drive innovative data science strategies.
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Salary
Base: $187,000.00 - $294,400.00; Bonus/Equity: Eligible for annual bonus and long-term incentive; Benefits: Comprehensive package including medical, dental, vision, 401(k), and paid time off
Skills & Requirements
Must-have
Ph.D. or MS in scientific discipline
7+ years pharmaceutical industry experience
Expert knowledge of Generative AI and LLMs
Experience with Graph Neural Networks (GNNs)
Domain knowledge in ADME and PK/PD modeling
Nice-to-have
Proven track record of leading high-performing teams
Publications in top-tier ML conferences like NeurIPS
Strong external collaboration with academic institutions
Ability to explain complex concepts to diverse audiences
Key Requirements
Ph.D. required with minimum 7 years experience OR MS with 10+ years
Expert-level technical skills in modern AI/ML and scientific domains
Demonstrated leadership in building and developing data science teams