Principal Scientist Data Science

Socket.dev

Cambridge (MA)

On-site

USD 117,000 - 201,000

Full time

14 days+

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Job summary

Johnson & Johnson is seeking a Principal Scientist to lead ML, GenAI, and optimization efforts across global clinical operations. The role focuses on enrollment forecasting, cost estimation, country/site selection, and advanced data techniques using real-world and clinical data.

Ideal candidates have a PhD and 5+ years of industry ML experience, with strong Python, MLflow, and optimization tooling. Collaborative leadership and ability to communicate complex results are essential.

Qualifications

Ph.D. in a quantitative discipline and 5+ years delivering data science projects with ML, optimization, NLP, and GenAI.
Hands-on multi-modal ML modeling and stochastic simulations for time-series forecasting.
Experience with GenAI and clinical LLMs for document parsing, harmonization, and data extraction.

Responsibilities

Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions for clinical trial ops.
Build ML models and optimization engines using operational, RWD, and cost data; forecast outcomes and optimize scenarios.
Adapt LLMs for information extraction, trial protocols comparison, data harmonization, and eligibility evaluation.
Conduct enrollment simulations to forecast study timelines and outcomes.
Communicate methods and results to diverse audiences to enable decision-making.
Coach junior colleagues in techniques and processes.

Skills

GenAI

MLOps

Python

SQL

Optimization

NLP

EHR data

Data Visualization

Communication

Education

Ph.D. in quantitative discipline

Tools

MLflow

Kedro

Git

CI/CD (Jenkins/GitLab)

DSPy

LangChain

pymoo

Scikit-learn

XGBoost

Optuna

PyMC

Job description

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

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:

Barcelona, Spain, Cambridge, Massachusetts, United States of America, Hyderabad, Andhra Pradesh, India, Madrid, Spain, 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 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.

Required Skills:

Preferred Skills: