Post Doc - AI/ML and Optimization

Johnson & Johnson Innovative Medicine; Titusville (NJ); On-site; USD 95,000 - 130,000; Full time

Job summary

Johnson & Johnson is recruiting a Postdoctoral Scientist for AI/ML and optimization in Global Development (DDSAI GD). You will apply ML, GenAI, and optimization to build computational solutions that support enrollment forecasting, cost estimation, and site/country selection in global clinical operations.

The role emphasizes developing reproducible ML workflows, validating models with real-world data, and communicating insights to cross-functional teams.

Qualifications

• PhD in computer science, statistics, biostatistics, biomedical informatics, epidemiology, health economics, applied mathematics, engineering, or other quantitative field.
• 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 MILP.
• Proficiency with 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, EHR and claims, and financial data.
• A record of scientific communication through publications, conference presentations, open-source contributions, or equivalent outputs.

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 and optimize scenarios.
• Develop and evaluate LLM-enabled workflows for document extraction, protocol comparison, and eligibility-criteria assessment.
• Perform stochastic simulations to forecast patient journey and trial outcomes.
• Design experiments and evaluation metrics to support validation, monitoring, data-quality, and responsible-AI practices.
• Contribute reusable code, data pipelines, and documentation with data scientists and engineers.
• Communicate findings clearly to technical and non-technical stakeholders and participate in scientific dissemination.

Skills

ML modeling

GenAI

Optimization

Python

SQL

LLM tools

Clinical data familiarity

Publications

Education

PhD in a quantitative field

Tools

MLflow

Kedro

Git

Jenkins

Job description

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function

Career Programs

Job Sub Function

Post Doc – Data Analytics & Computational Sciences

Job Category

Career Program

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, Titusville, New Jersey, United States of America

Job Description

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

J&J Innovative Medicine - Data, Data Science, and AI - Global Development (DDSAI GD) is recruiting a Postdoctoral Scientist - AI/ML and Optimization. The ideal candidate will apply machine learning (ML), generative AI (GenAI), and optimization techniques to develop computational solutions that support and enhance global clinical operations, including enrollment forecasting, cost estimation and optimization, and country and site selection.

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 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.

Required Qualifications

• 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.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.

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