Principal Scientist, Data Science

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are 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 more at

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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 Lead analytics ML optimization and GenAI that rely primarily on realworld data to inform clinical trial design feasibility and execution facilitation translate insights from RWD sources (e.g. EHR claims registries digital health) into clear recommendations that shape protocol decisions and operational plans.

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 responsibilities:

• Use RWD to quantify disease prevalence care pathways and the impact of inclusion/exclusion criteria; produce feasibility scoring across geographies sites and subpopulations.

• Assess RWDfeasible endpoints and proxies; evaluate availability completeness quality and signaltonoise to guide protocol design choices.

• Construct RWDbased cohorts and external/synthetic controls to benchmark protocol decisions and stresstest samplesize/timeline assumptions.

• Develop ML and multiobjective optimization solutions primarily powered by RWD to surface tradeoffs (speed quality cost diversity) and recommend design and operational scenarios informed by realworld care patterns.

• Build RWDcalibrated stochastic simulations of patient journeys to forecast timeline sensitivities and completion risk; provide RWD features calibration sets and feasibility constraints to the partner teams enrollment/screenfailure/retention models.

• Adapt LLMs/GenAI for structured extraction from RWD artifacts (structured and unstructured EHR notes radiology/pathology reports claims registries); harmonize concepts to standard vocabularies to support eligibility criteria evaluation and scheduleofactivities insights grounded in realworld practice.

• Clearly communicate RWDbased assumptions methods and results to clinical operational and leadership stakeholders; coach and mentor colleagues on RWD methodologies pipelines and best practices.

Required qualifications:

• A Ph.D. degree in quantitative discipline (e.g. computer science electrical and computer engineering biostatistics health economics biomedical informatics applied mathematics or similar)

• 5 years delivering ML/NLP/GenAI and multi objective optimization solutions with primary reliance on RWD (EHR claims registries digital health) including collaboration with operations analytics teams.

• Hands on experience with multimodal RWD (structured unstructured) predictive modeling and stochastic simulations for feasibility and time series scenario forecasting.

• Demonstrated ability to construct validate and deploy models from RWD to inform trial feasibility endpoint selection eligibility criteria effects and external control design.

• Experience building optimization engines (e.g. evolutionary algorithms reinforcement learning mixed integer linear programming) using RWD derived signals to navigate complex tradeoffs.

• Proficiency in MLOps (e.g. MLflow Kedro) Git and CI/CD; strong programming skills in Python and SQL; familiarity with DSPy/LangChain pymoo scikit learn XGBoost Optuna PyMC.

• Familiarity with healthcare privacy/compliance de identification practices and RWD data quality management; ability to integrate outputs from operational systems/models when needed while keeping the analytical core RWD driven.

Preferred qualifications:

• Demonstrated expertise applying RWD methods to inform trial design: target trial emulation propensity weighting/matching and survival/timetoevent analyses for endpoint feasibility and external/synthetic controls.

• Proven collaboration with operations analytics teams by supplying RWDderived cohorts features and feasibility evidence that improved enrollment forecasting site selection and diversity goals.

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 and 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 please email the Employee Health Support Center () or contact AskGS to be directed to your accommodation resource.

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