Postdoctoral Scientist - AI/ML and Optimization

• 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 1) predict outcomes of interest, 2) highlight tradeoffs between competing trial objectives, 3) recommend optimal operational scenarios, and 4) 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.
• 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.

• A 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.

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