Principal Scientist, Data Science

Johnson & Johnson

Principal Scientist leading analytics, machine learning, and optimization at Johnson & Johnson. Utilizing real-world data for clinical trial innovations and operational enhancements.

Posted 7/31/2026full-timeTitusville • Massachusetts, New Jersey, Pennsylvania • 🇺🇸 United StatesLead💰 $117,000 - $201,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in leveraging Real-World Data (RWD) for clinical trial design and optimization, with a strong focus on Machine Learning (ML), Natural Language Processing (NLP), and multi-objective optimization solutions. Proficient in constructing predictive models and simulations to inform trial feasibility and endpoint selection while ensuring compliance with healthcare standards.

Highest-signal resume keywords

Ph.D. In Quantitative DisciplineMachine Learning Solutions DevelopmentReal-World Data AnalysisPredictive Modeling ExperienceMLOps Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingMulti-Objective OptimizationPredictive ModelingStochastic SimulationsModel ValidationOptimization EnginesData AnalysisStatistical MethodsFeasibility Assessment

Soft Skills

Clear CommunicationCoaching and MentoringCollaboration

Tools & Technologies

PythonSQLMLflowKedroGitCI/CDDSPyLangChainPymooScikit-learn

Industry Keywords

Real-World DataHealthcare ComplianceDe-Identification PracticesClinical TrialsEndpoint SelectionOperational AnalyticsData Quality Management

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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.
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period
Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year