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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

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J&J Innovative Medicine - Data, Data Science, and AI - Global Development (DDSAI GD) is recruiting a Principal Scientist. The ideal candidate will leverage, adapt, and extend machine learning (ML), optimization techniques, and GenAI techniques to create computational pipelines supporting global clinical operations including enrollment forecasting, cost estimation and optimization, and country/site selection.

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, cardiovascular and metabolic disorders, immunology, pulmonary hypertension, neuroscience, and infectious disease. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market - from patients to practitioners and from clinics to hospitals. To learn more about J&J Innovative Medicine, visit https://www.jnj.com/innovative-medicine

Key responsibilities:

• Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions to support clinical trial operations.
• Leverage operational, RWD, and cost data to build ML predictive models and optimization engines to 1) predict outcomes of interest, 2) highlight tradeoffs between competing objectives, 3) recommend optimal operational scenarios, and 4) generate actionable insights, enabling early intervention and risk management.
• Adapt large language models (LLMs) for tailored information extraction and to create solutions including
  • conducting comparative analytics on clinical trial protocols and trial similarity assessment,
  • clinical trial data harmonization and standardization,
  • schedule of activity optimization, and
  • eligibility criteria evaluation,
• Stochastic enrollment simulations to forecast operational and patient journey outcomes including enrollment and study completion.
• Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making.
• Coaches and trains junior colleagues in techniques, processes, and responsibilities.

Required qualifications:

• A Ph.D. degree in a quantitative discipline (e.g., computer science, electrical and computer engineering, biostatistics, health economics, biomedical informatics, applied mathematics, or similar),
• 5+ years of industry experience delivering on data science projects using ML predictive modeling, multi-objective optimization, natural language processing, and GenAI.
• Hands-on experience with multi-modal ML predictive modeling and stochastic simulations for time-series forecasting.
• Experience building multi-objective optimization engines to navigate complex trade-offs using evolutionary algorithms, reinforcement learning, or mixed-integer linear programming.
• Experience with GenAI and clinical LLMs for document parsing and clinical concept disambiguation and harmonization.
• Proficient in MLOps practices and tools (MLflow, Kedro); Git usage, CI/CD stacks (Jenkins, GitLab) DevOps tools.
• 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),
• Demonstrated experience and familiarity with clinical operational data, real world data, electronic health records and claims, and financial data.

Preferred qualifications:

• Prior experience in a data science AI/ML role in healthcare, MedTech, and pharmaceutical industries.
• Hand-on experience utilizing clinical trial protocols, registry, cost data, CTMS, EDC and/or EHR to build ML models for estimating operational outcomes or RWE outcomes.

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