Post Doc - AI/ML and Optimization

Johnson & Johnson • Titusville, MA

6d • Hybrid

About The Position

J&J Innovative Medicine’s Data, Data Science, and AI - Global Development (DDSAI GD) is recruiting a Postdoctoral Scientist to apply machine learning, generative AI, and optimization techniques to develop computational solutions that support and enhance global clinical operations. Areas of application include enrollment forecasting, cost estimation and optimization, and country and site selection. The role involves research and development of analytical pipelines and tools, collaboration with data scientists, engineers, and clinical-development partners, and communication of findings and recommendations to technical and non-technical stakeholders. The scientist will also contribute to internal or external scientific dissemination.

Requirements

• Ph.D. 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 multimodal machine-learning predictive modeling and stochastic simulations for time-series forecasting.
• Experience with generative AI and clinical LLMs for document parsing, clinical-concept disambiguation, or harmonization.
• Experience building multi-objective optimization engines to navigate complex tradeoffs using evolutionary algorithms, reinforcement learning, or mixed-integer linear programming.
• Proficiency in Python and SQL.
• Experience with Python LLM tools such as DSPy or LangChain, optimization tools such as pymoo, and machine-learning tools such as Scikit-learn, XGBoost, Optuna, or 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.

Nice To Haves

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

Responsibilities

• Conduct research and build, test, and improve machine-learning, generative-AI, and optimization pipelines for clinical development and trial operations.
• Analyze, prepare, and use operational, real-world, and cost data to build machine-learning models and optimization engines that predict outcomes, highlight tradeoffs between trial objectives, recommend operational scenarios, and generate actionable insights for 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.
• Use 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, and documentation in collaboration with data scientists, engineers, and clinical-development partners.
• Communicate research findings, analytical methods, and recommendations to technical and non-technical stakeholders, and contribute to internal or external scientific dissemination.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree