Principal Scientist, Data Science

About this role

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

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: Data Analytics & Computational Sciences

Job Sub Function: Data Science

Job Category: Scientific/Technology

All Job Posting Locations: Cambridge, Massachusetts, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description: 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 real‑world 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 https://www.jnj.com/innovative-medicine

Key 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 RWD‑feasible endpoints and proxies; evaluate availability, completeness, quality, and signal‑to‑noise to guide protocol design choices.

• Construct RWD‑based cohorts and external/synthetic controls to benchmark protocol decisions and stress‑test sample‑size/timeline assumptions.

• Develop ML and multi‑objective optimization solutions primarily powered by RWD to surface trade‑offs (speed, quality, cost, diversity) and recommend design and operational scenarios informed by real‑world care patterns.

• Build RWD‑calibrated stochastic simulations of patient journeys to forecast timeline sensitivities and completion risk; provide RWD features, calibration sets, and feasibility constraints to the partner team’s enrollment/screen‑failure/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 schedule‑of‑activities insights grounded in real‑world practice.

• Clearly communicate RWD‑based 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/time‑to‑event analyses for endpoint feasibility and external/synthetic controls.

• Proven collaboration with operations analytics teams by supplying RWD‑derived 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

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