At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions 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.
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Post Doc – Data Analytics & Computational SciencesJob Category:
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Cambridge, Massachusetts, United States of America, Titusville, New Jersey, United States of AmericaJob 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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Required Skills:
Preferred Skills:
Skills Required
- 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 data harmonization.
- Experience building multi-objective optimization engines using evolutionary algorithms, reinforcement learning, or mixed-integer linear programming.
- Proficiency with Python and SQL.
- Experience with tools such as DSPy, LangChain, pymoo, scikit-learn, XGBoost, Optuna, and PyMC.
- Familiarity with clinical operational data, real-world data, electronic health records, claims, and financial data.
- Scientific communication through publications, conference presentations, open-source contributions, or equivalent research outputs.
- Experience with EHR, claims, registry, CTMS, EDC, or clinical-trial data and related privacy and data-quality practices.
- Experience with MLOps tools and practices, including MLflow or Kedro, Git, CI/CD, Jenkins, GitLab, and DevOps tools.
Johnson & Johnson Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Johnson & Johnson and has not been reviewed or approved by Johnson & Johnson.
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Healthcare Strength — Healthcare coverage is characterized as comprehensive across medical, dental, and vision, with added supports like onsite clinics, fitness centers, and Employee Assistance resources. Mental-health services and wellbeing reimbursements are also described as meaningful components of the overall package.
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Retirement Support — Retirement offerings are portrayed as a major differentiator, combining a 401(k) with employer matching and an employer-funded pension plan. Stock options and other long-term financial supports are also positioned as part of the broader rewards mix.
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Parental & Family Support — Family-related benefits are presented as notably strong, including paid parental leave for all new parents and additional leave types for caregiving and bereavement. Financial assistance for adoption, fertility treatment, and surrogacy is highlighted as a significant support.
Johnson & Johnson Insights
What We Do
Profound Change Requires Boldness. Johnson & Johnson is the largest and most broadly based healthcare company in the world. We’re producing life-changing breakthroughs every day, and have been for the last 130 years. The combination of new technologies and your expertise enables amazing things to happen. Teams from J&J’s consumer business are creating digital tools to help people track the health of their skin. Those working in medical devices are 3-D printing artificial joints personalized for each patient, while researchers in pharmaceuticals use AI to discover lifesaving drugs. Imagine what the rest of our team of 134,000 people at 260 companies in more than 60 countries across the world is accomplishing. We redefine what it means to be a big company in today’s world. Social Media Community Guidelines: http://www.jnj.com/social-media-community-guidelines
