Principal Data Scientist - AI/ML and Optimization

Johnson Johnson; Madrid; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

Johnson & Johnson Innovative Medicine develops treatments that improve health worldwide. Data science, AI, and GenAI drive clinical development across oncology, cardiometabolic, immunology, and infectious diseases.

Madrid is the primary location with global operations. They seek a Principal Data Scientist to lead ML, optimization, and GenAI initiatives for clinical operations, forecast enrollments, estimate costs, and optimize site selection, while mentoring juniors and communicating complex

Qualifikationen

• PhD in a quantitative discipline required.
• 5+ years of industry data science experience with ML, optimization, NLP and GenAI.
• Hands-on multi-modal ML and stochastic simulations for time-series forecasting.
• GenAI and clinical LLMs for document parsing and harmonization.
• Proficient in MLOps, Python and SQL.

Aufgaben

• Conceive, develop, and implement ML and GenAI solutions for clinical trial ops.
• Develop predictive ML models to forecast time-series KPIs.
• Formulate and solve optimization problems with trade-offs (cost, time, quality).
• Integrate modeling with optimization to compare scenarios and guide decisions.
• Coaches and trains junior colleagues in techniques and processes.

Kenntnisse

ML modeling

Optimization

GenAI

Python

SQL

MLOps

Communication

Ausbildung

PhD in quantitative field

Tools

MLflow

Kedro

Jenkins

GitLab

LangChain

Jobbeschreibung

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

Barcelona, Spain, Cambridge, Massachusetts, United States of America, 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 Data Scientist - AI/ML and Optimization. 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.The primary location is Madrid, Spain. Other listed locations will be considered if approved by the business.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, real-world (RWD), and cost data to develop ML models and optimization engines that support data-driven decision making across operational planning. Specifically:
• Develop predictive ML models to forecast operational time-series outcomes and KPIs.
• Formulate and solve optimization problems that quantify tradeoffs among competing objectives, such as cost, timelines, patient burden, quality, and operational efficiency.
• Integrate predictive modeling with optimization techniques to evaluate alternative operational scenarios, assess potential outcomes, and recommend optimal strategies.
• Build explainable decision-support systems that translate complex analytics into actionable insights, enabling proactive planning, early risk identification, and informed business decision making.
• 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.