Principal Scientist, Operations Research & Decision Science- R&D DDSAI - Therapeutics Development & Supply (TDS)

Johnson Johnson; Spring House (PA); On-site; USD 150,000 - 230,000; Full time

Job summary

Johnson & Johnson Innovative Medicine is recruiting a Principal Scientist, Operations Research & Decision Science to design, build, and scale simulation, optimization, digital twin, and decision-support capabilities across Therapeutics Development & Supply (TDS). The primary location for this position is open to Spring House, PA or Madrid, Spain.

The ideal candidate will translate complex development, manufacturing, portfolio, and clinical supply processes into rigorous computational models,

Qualifications

• Hands-on principal scientist with expertise in operations research and decision science.
• Experience translating development, manufacturing, portfolio, and clinical supply processes into computational models.
• Strong programming skills in Python and enterprise software deployment.

Responsibilities

• Formulate and solve optimization problems for portfolio prioritization and resource planning.
• Design, build, validate digital twins and simulations to evaluate future scenarios.
• Develop reusable modeling workflows and integrate models with enterprise data.
• Collaborate with cross-functional teams to drive decision support capabilities.

Job description

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

Madrid, Spain, Spring House, Pennsylvania, United States of America

Job Description

Johnson & Johnson Innovative Medicine is recruiting for Principal Scientist, Operations Research & Decision Science- R&D DDSAI - Therapeutics Development & Supply (TDS)

The primary location for this position is open to Spring House, PA or Madrid, Spain

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. 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 Johnson & Johnson Innovative Medicine visit https://innovativemedicine.jnj.com/

POSITION SUMMARY

The R&D Data, Data Science and Artificial Intelligence (DDSAI) organization is seeking a hands‑on Principal Scientist, Operations Research & Decision Science to design, build, and scale simulation, optimization, digital twin, and decision‑support capabilities across Therapeutics Development & Supply (TDS), including Chemistry, Manufacturing & Controls (CMC) and Clinical Supply Chain (CSC). This individual contributor will translate complex development, manufacturing, portfolio, capacity, resource, and clinical supply processes into rigorous computational models. The ideal candidate combines deep expertise in operations research, industrial engineering, mathematical modeling, and scientific computing with the software engineering discipline required to deploy trusted solutions in enterprise decision workflows.

KEY RESPONSIBILITIES

Operations Research & Mathematical Optimization

• Formulate and solve optimization problems involving portfolio prioritization, resource allocation, capacity planning, and resource scheduling.
• Apply linear and mixed‑integer programming, nonlinear optimization, stochastic programming, decomposition, heuristics, and multi‑objective methods as appropriate to the decision context.
• Develop models that make constraints, uncertainty, risk, and tradeoffs explicit and convert model outputs into clear decision recommendations.

Simulation, Digital Twins & Systems Modeling

• Design, build, validate, and maintain digital twins of TDS and CSC operations to evaluate future scenarios, policies, investments, and operating strategies.
• Develop discrete‑event, Monte Carlo, agent‑based, system dynamics, and hybrid simulations when appropriate to represent complex operational systems.
• Establish fit‑for‑purpose practices for model verification, validation, calibration, sensitivity analysis, uncertainty quantification, and ongoing performance monitoring.
• Create reusable modeling components and platforms that can be extended across products, programs, sites, and business processes.

Technical Delivery & Scaled Capability Development

• Develop robust, reusable modeling workflows and decision‑support products using Python and modern scientific computing, optimization, simulation, and cloud‑based tooling.
• Apply software engineering best practices, including modular design, version control, testing, documentation, reproducibility, code review, and maintainable interfaces.
• Partner with data engineering, product, architecture, and platform teams to integrate models with enterprise data and embed outputs into recurring planning and operational workflows.

Cross‑Functional Leadership & Business Impact

• Partner with leaders and subject‑matter experts across TDS, CSC, technical operations, portfolio management, finance, and digital organizations to define decisions, requirements, constraints, and measures of value.
• Lead technical work across multiple initiatives while remaining a hands‑on modeler and developer.
• Communicate model assumptions, limitations, results, and recommendations clearly to technical and non‑technical audiences.
• Mentor colleagues and