Sr. Data Scientist – AI Driven Operations Research
About this opportunity
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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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 378 comparable Senior Information Technology roles across 34 biopharma companies.
Salary context
45 of 378 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 378 comparable roles
+ 15 more countries
Seniority mix
378 of 378 peers have a known seniority level
Therapeutic area mix
0 of 378 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
No peers with a known therapeutic area yet.
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.