below marketmost similar roles pay hereabove market
This role pays more than
93%
of similar roles. Most pay
$142,250–$222,500
— the blue band above.
At the midpoint, this role pays about
$271k
versus about
$182k
for comparable roles.
Based on 240 similar postings.
Employer
About MSD
MSD (Merck Sharp & Dohme) is the international name for Merck & Co., a major U.S.-based pharmaceutical company.
MSD currently has
5 open roles
on FindRole.
Listed pay typically runs
$142,400–$224,100
across 5 roles with salary data.
JOB TITLE: Dist. Scientist, Research Science
The Dist. Scientist, Research Science will provide scientific and strategic leadership for Brick 1 within the CMC Lighthouse Pillar 2, Digitally Enabled Technology Transfer. This role involves defining and executing an integrated strategy to establish, mature, and deploy models across CMC domains and modalities. The individual will lead product, process, analytical, manufacturing, and technology-transfer modeling capabilities to improve process understanding and accelerate transfer decisions. Key responsibilities include defining the model landscape, integrating cross-modality strategies, and managing model lifecycles. The position requires expertise in mechanistic, statistical, hybrid, empirical, and AI/ML modeling approaches. Candidates must possess skills in CMC strategy, data analysis, and scientific leadership to solve complex technology-transfer problems, ensuring predictive and consistent results across development, manufacturing, and commercialization workflows within a regulated pharmaceutical environment.
Define and lead an integrated strategy for establishing and deploying models across CMC domains and modalities.
Establish a cross-modality framework to determine required models and identify opportunities for reuse.
Guide the development of mechanistic, statistical, hybrid, empirical, and AI/ML models with clear validation criteria.
Create a transparent inventory and gap assessment of existing and required models across the CMC portfolio.
Prioritize modeling investments based on technology-transfer risk, scientific value, and readiness for deployment.
Lead cross-functional teams through model concept, development, qualification, implementation, and sustainment.
Establish governance for model documentation, traceability, verification, validation, and lifecycle oversight.
Develop playbooks and standards to accelerate model adoption and reduce reinvention across the organization.
What we're looking for
Ph.D. in chemical engineering, chemistry, biochemistry, pharmaceutical sciences, systems engineering, applied mathematics, data science, or a related field.
More than 15 years of relevant industry experience or an equivalent combination of education and experience.
Recognized scientific or technical leadership in CMC development, technology transfer, manufacturing sciences, process development, analytical development, or model-informed development.
Demonstrated success defining and leading complex, cross-functional technology strategies or portfolios from concept through sustained implementation.
Strong understanding of CMC development-to-manufacturing handoffs, scale-up, control strategy, process and analytical models, and regulated product development.
Broad knowledge of mechanistic, statistical, hybrid, empirical, and AI/ML modeling approaches and their fit-for-purpose application across CMC domains.
Ability to communicate complex technical concepts clearly to scientists, engineers, modelers, quality and regulatory partners, and senior executives.
Track record of influencing across organizational boundaries and resolving ambiguity, competing priorities, and complex dependencies.
Predictive Modeling
Machine Learning
AI
Data Analytics
Statistical Analysis
DOE
Laboratory Automation
Digital Transformation
Simulation
Data Science
R&D
Advanced Analytics
Machine Learning
PyTorch
Python
Deep Learning
Information Retrieval
Recommender Systems
Language Models
Multi-GPU
Google Cloud
AWS
Azure
Representation Learning
Graph Data
Preference Optimization
Generative AI
Machine Learning
Deep Learning
Computer Vision
Computer Graphics
Natural Language Processing
Diffusion Models
Large Language Models
Multimodal Foundation Models
Video Generation
Video Editing
Machine Learning
Digital Signal Processing
Estimation Theory
Controls
Numerical Analysis
Differential Equations
Linear Algebra
Optimization
Scientific Computing
Agile
Machine Learning
Digital Signal Processing
Estimation Theory
Controls
Numerical Analysis
Differential Equations
Linear Algebra
Optimization
Scientific Computing
Agile
Software Design
Modeling and Simulation
Compute Clusters
Statistics
Probability
Machine Learning
Digital Signal Processing
Estimation Theory
Controls
Numerical Analysis
Differential Equations
Linear Algebra
Optimization
Scientific Computing
Agile
Software Design