Senior Data Scientist, Agentic AI and Machine Learning

We are seeking an exceptional Agentic AI and Machine Learning expert for the position of Senior Scientist, Data Science within our Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) department.

This role is responsible for the development, benchmarking, deployment and integration of world‑class agentic AI and Machine Learning in PDMB. It requires partnering closely with world‑class scientists and pioneering the use of cutting‑edge AI/ML innovations that augment scientific insight, streamline workflows and improve decision‑making across the drug development lifecycle to advance transformative medicines.

The ideal candidate will have a strong technical background and an aptitude for speaking the languages of science, technology and business strategy to deliver human‑in‑the‑loop AI/ML. You will further augment critical partnerships with stakeholders in internal R&D functions and with internal and external partners.

• Act as a trusted technical partner to DMPK scientists, clinical pharmacologists, statisticians, clinicians, and research leaders.
• Facilitate cross‑functional alignment, and translate scientific and operational needs into clear AI/ML solution requirements.
• Communicate clearly with both technical and non‑technical audiences, explaining capabilities, limitations, and trade‑offs.

• Design, develop, benchmark and deploy AI agents to support PDMB and clinical workflows, including automated report generation, quality evaluation, consistency checks, process monitoring and deviation detection, scheduling, prioritization, and alerting systems.
• Apply agent development frameworks and architectures (e.g., tool‑using agents, workflow agents, human‑in‑the‑loop systems).
• Integrate agents and ML methods into existing R&D platforms, laboratory systems, data lakes, and clinical data environments.

• Develop and apply machine learning and deep learning models for DMPK and clinical applications, including building and evaluating simulation and hybrid ML‑mechanistic models to support decision‑making in discovery and development and applying best practices in model validation, benchmarking, uncertainty estimation, and performance monitoring.

• Define benchmarks and success metrics for AI agents and ML models, including scientific quality, operational efficiency, and user adoption.
• Implement ongoing monitoring for model drift, data quality, agent behavior, and downstream impact.
• Contribute to responsible AI practices, including transparency, reproducibility, governance, and compliance with GxP considerations.

• Define and track value metrics such as time savings, cost avoidance, throughput improvements, and decision quality.
• Quantify and communicate return on investment (ROI) and business impact from deployed AI and ML solutions.
• Support prioritization of AI initiatives based on scientific impact, feasibility, and value creation.

• Strong foundation in machine learning algorithms, including deep learning, supervised/unsupervised learning, and time‑series or sequence modeling.
• Experience with foundation models (e.g., large language models, multimodal models) and techniques for adaptation (prompting, fine‑tuning, retrieval‑augmented generation).
• Practical experience with AI agent frameworks, orchestration, and tool integration.
• Expertise in model evaluation and benchmarking, including offline metrics and real‑world performance monitoring.
• Proven ability to work with large‑scale, heterogeneous datasets (biological, chemical, clinical, operational).
• Proficiency in Python and modern ML/data science tooling; experience with scalable data and model deployment environments.

• Demonstrated strength in stakeholder management and influencing without authority.
• Experience driving consensus in cross‑functional teams spanning science, engineering, and operations.
• Ability to independently lead initiatives from problem definition through deployment and impact measurement.
• Strong written and verbal communication skills.