AI Data Scientist

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.

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, Global Supply Chain is seeking a Sr. Data Scientist – Data Products & Supply Chain Analytics to develop reusable, governed, and scalable analytics and data products that support supply chain, manufacturing, planning, logistics, clinical supply, and operations decision-making.

ROLES & RESPONSIBILITIES

Responsibilities will include, but are not limited to: Data product and analytics solution delivery: Develop reusable analytics and data science solutions for supply chain, manufacturing, planning, logistics, clinical supply, and operations business challenges. Data preparation and integration: Gather, clean, transform, and integrate data from multiple sources to create reliable datasets for analysis, modeling, visualization, feature engineering, and decision support. Reusable analytical workflows: Build reproducible analytical workflows using Python, R, SQL, or similar tools, moving analyses beyond notebooks into maintainable, governed, and reusable solutions. Cloud data platform analytics: Use platforms such as Databricks, Snowflake, or similar cloud data environments to prepare large datasets, develop scalable analytics, and support reusable analytical workflows. Digital team partnership and data product translation: Partner closely with product owners, data engineers, platform teams, technology teams, and business stakeholders to understand user needs, define analytical questions, translate needs into data product requirements, interpret results, and turn insights into action. Analytics application enablement: Partner with technology teams to help deliver analytical and decision-support capabilities through modern application patterns, APIs, cloud deployment approaches, or web-based tools where appropriate. Communication and decision translation: Communicate analytical methods, assumptions, findings, recommendations, uncertainty, and implementation tradeoffs clearly to technical and non-technical audiences. Analytical excellence and continuous improvement: Promote strong analytical practices, including documentation, version control, testing, validation, reproducibility, reusable methods, technical curiosity, and continuous improvement.