Sr. Data Scientist – AI Driven Operations Research

Career Category

Supply Chain

Job 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

.