Principal Data Scientist: GenAI, RWD & Trial Analytics

Biopharma Asia; New Jersey; On-site; USD 117,000 - 201,000; Full time

Job summary

Johnson & Johnson’s Innovative Medicine division is seeking a Principal Data Scientist to lead end-to-end delivery of analytic solutions across clinical development and trial operations. You will own solution definition, delivery, adoption, and outcomes using ML, GenAI, optimization, and RWD.

Responsibilities include leading predictive modeling, simulations, and optimization for study feasibility, country/site selection, protocol design, and planning.

Qualifications

• Master’s degree in a quantitative field; PhD preferred.
• At least 5 years of industry experience leading end-to-end data-science projects.
• Experience defining, validating, deploying, and driving adoption of analytical solutions.
• Proficiency in Python and SQL, modern ML, optimization, and LLM-development libraries, and reproducible MLOps practices.

Responsibilities

• Define end-to-end data-science solutions for defined business problems.
• Lead predictive modeling, simulations, optimization, and GenAI applications for study feasibility and planning.
• Apply clinical, operational, cost, and real-world data to generate evidence for populations and pathways.
• Lead RWD analytics informing protocol feasibility, recruitment, and study design.
• Develop LLM-enabled workflows for information extraction, data harmonization, and automation.
• Partner with stakeholders to frame questions and translate outputs into adopted recommendations.

Skills

Advanced Analytics

Data Visualization

Data Science

Data Analysis

Python

SQL

LLM Development

MLOps

Stakeholder Management

Education

Master's degree in quantitative field

PhD preferred

Tools

Python tooling

Job description

Johnson & Johnson’s Innovative Medicine division is seeking a Principal Data Scientist to lead end-to-end delivery of analytic solutions across clinical development and trial operations. You will own solution definition, delivery, adoption, and outcomes using ML, GenAI, optimization, and RWD.

Responsibilities include leading predictive modeling, simulations, and optimization for study feasibility, country/site selection, protocol design, and planning.