Sr. Data Scientist – Data Sciences & AI

Career Category Supply Chain

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.

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 an experienced applied AI Data Scientist to join our team. As a Senior AI Data Scientist, you will be responsible for designing, developing, and deploying complex software applications on clinical and commercial supply chain processes. This role will be actively collaborating with a wide range of business leaders, designing and implementing sophisticated analytical models. You will work closely with cross-functional teams to deliver high-quality, scalable, and maintainable solutions. ROLES &

Responsibilities

will include, but are not limited to: Rapid prototyping and AI solution development: Quickly translate business concepts, scientific questions, and product ideas into working AI prototypes, production-ready code, and scalable digital capabilities. AI/ML, Generative AI, and advanced modeling delivery: Develop innovative AI/ML solutions using Large Language Models, Generative AI, foundation models, supervised and unsupervised learning, and other advanced modeling techniques to support supply chain decision-making and automation. AI application integration and automation: Integrate AI capabilities into Global Supply Chain applications, APIs, workflows, assistants, copilots, and automation platforms using context engineering, tool integration, and emerging approaches such as Model Context Protocol to enable real-time intelligence, productivity, operational efficiency, and improved user experience. End-to-end AI and data science ownership: Own complex AI, data science, and decision-support solutions from problem framing through prototype, validation, deployment, and stabilization, managing scope, risks, dependencies, timelines, technical tradeoffs, and measurable outcomes such as cycle time, forecast quality, data quality, and operational efficiency. Technical documentation, quality, and problem-solving: Create clear documentation for prototypes and solutions, including design choices, data flows, AI workflows, assumptions, limitations, and key implementation details; identify and resolve technical challenges effectively. Technology evaluation and learning agility: Stay current with emerging AI technologies, frameworks, industry trends, and engineering practices, demonstrating the ability to assess options, conduct due diligence, and make informed technology recommendations based on business value, technical fit, and implementation impact. Product and module ownership: Develop a strong understanding of the overall product, its modules, dependencies, AI components, and user workflows while serving as a technical expert for assigned components or solution areas. Cross-functional collaboration and stakeholder partnership: Work closely with product teams, business teams, technology teams, architecture teams, AI platform teams, data teams, stakeholders, and subject-matter experts to deliver practical, scientific, data-driven solutions aligned with enterprise standards.