below marketmost similar roles pay hereabove market
This role pays more than
52%
of similar roles. Most pay
$162,000–$221,000
— the blue band above.
At the midpoint, this role pays about
$186k
versus about
$192k
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
56 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 43 roles with salary data.
The Senior Principal Scientist, Data Science - R&D DDSAI - Therapeutics Development & Supply (TDS) joins the R&D Data, Data Science & AI organization as a senior individual technical contributor. This role focuses on developing and adopting Operations Research and Decision Intelligence solutions across Chemistry, Manufacturing and Controls, device development, and clinical supply chains. You will design, build, and validate forecasting, optimization, simulation, and decision-modeling capabilities for portfolio planning, laboratory digital twins, and clinical supply. Key responsibilities include developing reusable modeling frameworks, quantifying uncertainty, and providing technical mentorship. Required skills include advanced expertise in mathematical optimization, linear and mixed-integer programming, Monte Carlo or discrete-event simulation, and Python. You will solve complex scientific and operational problems by connecting demand, workflow, and capacity to actionable decisions within a regulated pharmaceutical environment.
Design, build, and validate forecasting, optimization, simulation, and decision-support solutions for complex scientific and operational problems.
Develop long-term and short-term portfolio, workload, resource, and capacity forecasts while quantifying uncertainty and scenario sensitivities.
Create business operations analytics and laboratory or process digital twins to connect demand, workflow, and equipment capacity to actionable decisions.
Build reusable modeling frameworks and reference implementations for mathematical optimization, simulation, and uncertainty quantification.
Apply disciplined software practices including modular code, version control, testing, documentation, and peer review to all model development.
Provide technical guidance and mentorship to data scientists on problem formulation, model architecture, code review, and validation.
Partner with technology teams on data integration, deployment, observability, security, and lifecycle ownership for data products.
Communicate technical recommendations, assumptions, and limitations clearly to senior leaders and business stakeholders to drive solution adoption.
What we're looking for
Ph.D. in a quantitative discipline with 8+ years of relevant experience or a master’s degree with 10+ years of relevant experience.
Advanced expertise in mathematical optimization, simulation (Monte Carlo or discrete-event), and forecasting and uncertainty analysis.
Strong hands-on Python skills and demonstrated use of modern software-development and model-validation practices.
Track record of delivering quantitative decision-support solutions that achieve sustained adoption and reuse.
Demonstrated technical leadership through solution architecture, methodological improvement, standards, or mentorship.
Ability to formulate problems with subject matter experts and communicate assumptions, trade-offs, and limitations clearly.
Experience in pharmaceutical R&D, CMC development, laboratory operations, clinical supply, or manufacturing (preferred).
Experience with portfolio forecasting, capacity planning, digital twins, or stochastic programming (preferred).
Python
Scikit-learn
Pandas
NumPy
PyTorch
TensorFlow
AWS
Snowflake
Machine Learning
AI
Statistical Modeling
Version Control
Data Visualization
Bayesian Methods
Experimental Design
Business Intelligence
Data Science
Scientific Computing
Python
R
SQL
Machine Learning
Data Engineering
AWS
Snowflake
Redshift
Workflow Orchestration
Version Control
Predictive Modeling
Data Visualization
Business Intelligence
Ontologies
CDISC
SEND
AWS
Data Architecture
Data Modeling
Data Engineering
GraphRAG
Knowledge Graphs
Data Lakes
Lakehouses
Metadata Management
Data Governance
SDTM
ADaM
MedDRA
FHIR
OMOP
DICOM
Distributed Data Processing
FAIR Data Principles
Lucerne, Schachen (Werthenstein), Switzerland+314 days ago
Python
Machine Learning
Databricks
SQL
Operations Research
MLOps
GitHub
Copilot Studio
Data Engineering
Data Science
Agentic AI
VS Code
Dataverse
Version Control
Discrete Event Simulation
Stochastic Optimization