Senior Specialist, Data Science

Overview 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. 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 Methodologies (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 Toxicology Uncertainty Quantification Preferred Skills Relevant publication history in AI/ML for life sciences or toxicology Experience with regulator-ready model development Salary and Benefits 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. The successful candidate will be eligible for annual bonus and long-term incentive, if applicable. We offer a comprehensive package of benefits, including medical, dental, vision, retirement benefits (401(k)), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at the company career site. Additional Information We are an Equal Employment Opportunity Employer. We comply with applicable equal opportunity laws and provide accommodations in the hiring process upon request. Applicants should be aware of applicable state and local fair chance regulations where applicable. #J-18808-Ljbffr Merck