Post Doc, AI/ML and Optimization

About the Role

Johnson & Johnson Innovative Medicine is recruiting a Postdoctoral Scientist - AI/ML and Optimization. This role will apply machine learning (ML), generative AI (GenAI), and optimization techniques to develop computational solutions that support and enhance global clinical operations.

Key Responsibilities

• Conduct research, build, test, and improve ML, GenAI, optimization pipelines for clinical development and trial operations.
• Analyze, prepare, and leverage operational, RWD, and cost data to build ML models and optimization engines to:
• Predict outcomes of interest.
• Highlight tradeoffs between competing trial objectives.
• Recommend optimal operational scenarios.
• Generate actionable insights, enabling early intervention and risk management.
• Develop and evaluate LLM-enabled workflows for document extraction, protocol comparison, trial similarity assessment, data harmonization, clinical-concept interpretation, and eligibility-criteria assessment.
• Conduct stochastic simulations to forecast operational and patient journey outcomes including enrollment and patient study completion.
• Design experiments and tailored evaluation metrics and frameworks to support validation, monitoring, data-quality, and responsible-AI practices.
• Contribute reusable code, data pipelines, documentation in collaboration with data scientists, engineers, and clinical-development partners.
• Communicate research findings, analytical methods, and recommendations clearly to technical and non-technical stakeholders and contribute to internal or external scientific dissemination.

Requirements & Tech Stack

• Ph.D. degree, completed or expected before the start date, in computer science, statistics, biostatistics, biomedical informatics, epidemiology, health economics, applied mathematics, engineering, or another quantitative discipline.
• Hands-on experience with multi-modal ML predictive modeling and stochastic simulations for time-series forecasting.
• Experience with GenAI and clinical LLMs for document parsing, clinical concept disambiguation, or harmonization.
• Experience building multi-objective optimization engines to navigate complex trade-offs using evolutionary algorithms, reinforcement learning, or mixed-integer linear programming.
• Proficiency with programming languages Python and SQL.
• Experience with Python LLM tools (e.g., DSPy, LangChain), optimization tools (e.g., pymoo), and ML tools (e.g., Scikit-learn, XGBoost, Optuna, PyMc).
• Familiarity with clinical operational data, real world data, electronic health records and claims, and financial data.
• A record of scientific communication through publications, conference presentations, open-source contributions, or equivalent research outputs.

Preferred Qualifications

• Experience with EHR, claims, registry, CTMS, EDC, or clinical-trial data and related privacy and data-quality practices.
• Experience in MLOps practices and tools (MLflow, Kedro); Git usage, CI/CD stacks (Jenkins, GitLab) DevOps tools.