Sr. Data Scientist – AI Driven Operations Research

Career CategorySupply ChainJob Description
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller, and longer. We discover, develop, manufacture, and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting edge of innovation, using technology and human genetic data to push beyond what is known today.
ABOUT THE ROLE
Role Description
Global Supply Chain (GSC) is accountable for orchestrating end-to-end supply chain strategies and operations that ensure reliable, timely delivery of medicines to patients — powered by data, innovation, and enterprise-wide collaboration.
As part of our team expansion at Amgen India (AIN), GSC is seeking a highly analytical and technically strong Sr. Data Scientist – AI Driven Operations Research to join our team. This role focuses on applying operations research, digital twin modeling, simulation, optimization, and advanced analytics to solve complex clinical and commercial supply chain challenges across drug development, manufacturing, distribution, and patient delivery operations.
The ideal candidate combines strong quantitative modeling expertise with hands-on software engineering capabilities to develop scalable, production-ready analytical solutions that support strategic and operational decision-making under uncertainty.
ROLES & RESPONSIBILITIES
Core Responsibilities
Supply Chain Digital Twin Development
Support the design, development, and deployment of Digital Twin capabilities for Supply Chain operations.
Develop solutions that combine:
Simulation modeling
Optimization methods
Machine learning techniques
Scenario planning capabilities
Decision intelligence frameworks
Help evaluate system behavior under varying conditions and operational constraints.
Decision Support & Scenario Analysis
Build scalable decision-support solutions that enable:
What-if analysis
Scenario planning
Capacity and resource planning
Supply chain resilience assessment
Strategic tradeoff evaluation
Risk-based decision making
Translate analytical outputs into actionable recommendations for business stakeholders.
Optimization & Simulation Technology
Develop expertise in optimization and simulation technologies including:
AnyLogic (preferred)
Simio
Arena
Other simulation platforms
Gurobi
OR-Tools
Related optimization technologies
Evaluate emerging tools and methodologies that can improve decision support capabilities.
AI-Enabled Analytics
Leverage AI-assisted tools and development practices to accelerate analytical solution delivery.
Apply AI technologies to:
Analytical modeling
Software development
Code quality improvement
Productivity enhancement
Decision-support solution development
Explore opportunities to incorporate machine learning and AI into operational decision systems.
Technical Standards & Reusable Frameworks
Contribute to enterprise analytical capabilities through development of:
Reusable optimization frameworks
Simulation modeling patterns
Decision-support accelerators
Analytics best practices
Modeling standards
Technical methodologies
Promote consistency and scalability across analytical solutions.
Collaboration & Stakeholder Partnership
Partner closely with:
Product teams
Business teams
Data engineering teams
Technology teams
Enterprise architecture teams
Supply chain subject matter experts
Translate business challenges into analytical solutions aligned with enterprise standards and strategic objectives.
Communication & Decision Translation
Communicate analytical findings and technical concepts to both technical and non-technical audiences.
Clearly articulate:
Model assumptions
Risks and uncertainty
Analytical findings
Solution limitations
Implementation tradeoffs
Recommended actions
Support informed decision-making through effective storytelling and visualization.
Core Competencies
Hands-on experience with:
Operations Research and Optimization
Simulation Modeling
Python Application Development
Supply Chain Analytics
Scenario Planning
Decision Support Systems
Strong understanding of:
Inventory Management
Demand Planning
Production Planning
Logistics Optimization
Enterprise Data Architecture
SQL and Data Engineering Concepts
AI and Machine Learning Applications
Ability to operate effectively in complex, cross-functional environments and translate advanced quantitative analyses into business impact.
Qualifications
Basic Qualifications
BS, MS, or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Data Science, or related quantitative field
Strong analytical and quantitative problem-solving skills
Advanced Python programming experience
Experience developing analytical models and decision-support solutions
Experience working with large-scale data-driven applications
Preferred Qualifications
Experience with Digital Twin platforms and simulation technologies
Production software development experience
Knowledge of supply chain planning and operations
Familiarity with SQL and enterprise data architectures
Exposure to machine learning and AI-enabled decision systems
Experience in pharmaceutical or life sciences environments
Familiarity with GxP, HIPAA, or related regulatory requirements
Core Success Measures
Delivery of scalable optimization and simulation solutions
Adoption of Digital Twin capabilities by supply chain teams
Improvement in operational decision quality
Increased supply chain resilience and planning effectiveness
Development of reusable analytical assets and frameworks
Effective collaboration with business and technical stakeholders
Translation of complex analytical insights into business value
.