This role pays less than
58%
of similar roles. Most pay
$133,920–$214,725
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$174k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
71 open roles
on FindRole.
Listed pay typically runs
$109,000–$177,100
across 65 roles with salary data.
Principal Scientist, Operations Research & Decision Science- R&D DDSAI - Therapeutics Development & Supply (TDS) joins the R&D Data, Data Science and Artificial Intelligence organization to design, build, and scale simulation, optimization, digital twin, and decision-support capabilities. This individual contributor translates complex manufacturing, portfolio, capacity, and clinical supply processes into rigorous computational models. The role involves formulating optimization problems for resource allocation and scheduling using linear/mixed-integer programming, stochastic programming, and heuristics. You will develop digital twins and various simulation types, including discrete-event and Monte Carlo methods, to evaluate operational strategies. Key technical requirements include proficiency in Python and experience with frameworks like Pyomo, Gurobi, CPLEX, OR-Tools, SimPy, AnyLogic, or Simio. The work addresses critical challenges in Chemistry, Manufacturing & Controls and Clinical Supply Chain to improve how the organization plans and executes its development portfolio.
What does a Scientist earn?
Median $145116 from 34 postings across 8 companies.
Formulate and solve optimization problems for portfolio prioritization, resource allocation, capacity planning, and scheduling.
Develop digital twins and simulation models to evaluate future scenarios and operational strategies for supply chain processes.
Build robust, reusable modeling workflows using Python and modern scientific computing tools like Pyomo or Gurobi.
Apply software engineering best practices including modular design, version control, and automated testing to ensure model reliability.
Integrate computational models with enterprise data systems to embed results into recurring planning and operational workflows.
Translate complex manufacturing and clinical supply processes into rigorous mathematical models and decision-support products.
Communicate technical findings, assumptions, and recommendations clearly to both technical and non-technical stakeholders.
Provide technical direction and mentorship to internal teams, consultants, and external collaborators on modeling initiatives.
What we're looking for
Advanced degree in Operations Research, Industrial Engineering, Systems Engineering, Applied Mathematics, Management Science, Computer Science, Chemical Engineering, or a related quantitative field.
5+ years of experience applying optimization, simulation, stochastic modeling, decision analysis, or other operations research methods to complex problems.
Demonstrated depth in at least two areas: mathematical programming, discrete-event simulation, stochastic optimization, scheduling, network optimization, resource allocation, inventory optimization, or decision-making under uncertainty.
Strong Python programming skills and experience with frameworks like Pyomo, Gurobi, CPLEX, OR-Tools, SimPy, AnyLogic, Simio, Arena, or equivalent tools.
Proven record of delivering computational models or decision-support products that influenced business or operational decisions.
Ability to provide technical leadership in cross-functional environments as a hands-on individual contributor.
Ph.D. in Operations Research, Industrial Engineering, Systems Engineering, Applied Mathematics, Management Science, or a closely related discipline (preferred).
Experience in pharmaceutical development, manufacturing, supply chain, or other complex operational environments (preferred).
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