Sr. Data Scientist – AI Driven Operations Research

Amgen · Healthcare & Pharma

India - Hyderabad
On-site
Posted today
Data & AI

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About the job

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

Responsibilities will include, but are not limited to:

Operations research and advanced modeling: Design and implement advanced analytical models using operations research techniques such as Mixed Integer Linear Programming, Discrete Event Simulation, Monte Carlo simulation, stochastic modeling, and decision analysis under uncertainty.

Decision-support and scenario analysis: Build scalable decision-support tools for scenario analysis, operational planning, resilience assessment, and strategic tradeoff evaluation across Global Supply Chain use cases.

Supply chain performance and resilience analytics: Analyze supply chain performance under uncertainty and provide data-driven recommendations to improve service levels, operational efficiency, risk mitigation, and supply chain resilience.

Optimization, simulation, and digital twin development: Support the development and deployment of enterprise digital twin capabilities for clinical supply chain operations, combining simulation, optimization, and machine learning techniques to enhance decision intelligence.

Analytical application development: Architect, develop, and maintain modular Python-based analytics, optimization, and simulation applications with strong engineering practices for maintainability, testing, performance, and scalability.

AI-assisted analytics and development: Leverage AI-assisted coding, analytics, and productivity tools to accelerate development, improve code quality, and enhance analytical solution delivery.

Technology evaluation and learning agility: Explore emerging technologies, digital twin platforms, optimization solvers, simulation tools, and analytical methods applicable to clinical supply chain and operational decision-making.

Technical standards and reusable frameworks: Contribute to technical standards, reusable modeling frameworks, simulation patterns, optimization methods, and analytics best practices across the organization.

Cross-functional collaboration and stakeholder partnership: Work closely with product teams, business teams, technology teams, data engineering teams, architecture teams, stakeholders, and subject-matter experts to deliver practical, scientific, data-driven solutions aligned with enterprise standards.

Communication and decision translation: Communicate technical concepts, model assumptions, analytical findings, uncertainty, limitations, and implementation tradeoffs clearly to both technical and non-technical audiences.

Preferred Qualifications

Advanced degree (MS or PhD preferred) in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Data Science, or related quantitative field.

Experience with digital twin platforms or simulation tools such as AnyLogic (preferred), Simio, Arena, or similar technologies

Experience with Optimization solvers (Gurobi, CPLEX, OR-Tools, etc.)

Strong programming experience in Python for analytical application development.

Experience building production-quality analytical software and data-driven applications.

Familiarity with SQL, data engineering concepts, and enterprise data architecture

Knowledge of inventory management, demand planning, production planning, or logistics optimization.

Familiarity with pharma or life sciences compliance requirements (GxP, HIPAA) is a plus.

Exposure to machine learning, predictive analytics, or AI-enabled operational decision systems.

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