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Senior Specialist, Data Science
Merck
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About this role
Job Description
The Computational Toxicology Group within Nonclinical Drug Safety (NDS) seeks a senior AI/ML scientist to drive the development and deployment of next-generation computational toxicology capabilities. This role will combine advanced machine learning, foundation model engineering, and domain expertise to accelerate safer drug discovery and support regulatory-ready New Approach Methodologies (NAMs). The successful candidate will lead cross-functional projects, deliver production-grade models and agentic systems, and help establish governance and MLOps practices that ensure reproducibility, transparency, and ethical AI use in preclinical research.
Key responsibilities • Lead deployment of advanced AI/ML solutions (multimodal transformers, graph or sequence models, Bayesian/probabilistic approaches) for toxicity prediction and translational safety applications.
• Design and implement agentic AI systems tailored to toxicology use cases
• Specialize in the fine-tuning and alignment of foundation models for toxicology domain-specific applications and supporting new approach methods (NAMs).
• Drive collaboration with cross-functional teams of toxicologists, computational scientists, biologists, and chemists to ensure explainability, reproducibility, and address specific " context of use " regulatory requirements for safety assessments.
• Champion best practices in model governance, and responsible AI within a regulated environment, helping to establish frameworks for responsible and ethical AI deployment in preclinical research.
• Present and communicate science in key internal and external toxicology forums.
Required qualifications • Ph.D. or M.S. in Computer Science, Computational Biology, Computational Chemistry, Bioinformatics, Statistics, or related field.
• 0+ years post-PhD or 3+ years post-MS experience developing and deploying AI/ML models
• Hands-on experience with large language models and agentic AI frameworks (fine-tuning, prompt engineering, multi-agent orchestration, tool use, and API-based production orchestration) required.
• Proven experience integrating and modeling multimodal datasets (omics, chemical, textual, imaging).
• Strong software development skills in Python and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow), MLOps tools, cloud platforms (AWS preferred), and HPC environments.
• Excellent communication skills; ability to translate complex technical work to domain experts and leadership.
Preferred qualifications • Demonstrated publication record applying AI/ML to life sciences or toxicology.
• Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference.
• Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement learning for agent behavior control.
• Prior experience working in regulated environments or developing regulator-ready models.
Required Skills: Computational Biology, Computational Chemistry, Data Engineering, Data Modeling, Data Science, Data Visualization, Environmental Toxicology, Foundation Engineering, Large Language Models (LLMs), Machine Learning (ML), Machine Learning Operations, Prompt Engineering, Regulatory Requirements, Software Development, Stakeholder Relationship Management, Toxicology, Uncertainty Quantification Preferred Skills:
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US and Puerto Rico Residents Only:
Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please click here if you need an accommodation during the application or hiring process. As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit:
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We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, perspectives, skills and backgrounds. The fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another’s thinking and approach problems collectively.
Learn more about your rights, including under California, Colorado and other US State Acts
The salary range for this role is $129,000.00 - $203,100.00 This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.
The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.
We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at https://jobs.merck.com/us/en/compensation-and-benefits .
You can apply for this role through https://jobs.merck.com/us/en (or via the Workday Jobs Hub if you are a current employee). The application deadline for this position is stated on this posting.
San Francisco Resident
Salary insight
The midpoint of this range ($166k) is about 17% below the median disclosed salary for San Francisco roles listed on ForgeApply ($201k across 7,187 jobs).
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Based on live postings with disclosed pay on ForgeApply; refreshed daily. Not an estimate of this employer's offer.
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