AI Data Scientist Full-time

Sr. Data Scientist – Data Products & Supply Chain Analytics

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, 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

• Data product and analytics solution delivery
• Data preparation and integration
• Reusable analytical workflows
• Cloud data platform analytics
• Digital team partnership and data product translation
• Analytics application enablement
• Communication and decision translation
• Analytical excellence and continuous improvement

Preferred Qualifications

Preferred qualifications include a Master's degree or PhD in Data Science, Computer Science, Statistics, Operations Research, Engineering, Supply Chain Analytics, Applied Mathematics, or a related quantitative field. Experience applying data science, statistics, forecasting, simulation, optimization, machine learning, or visualization methods to complex business problems. Strong programming skills in Python or R, with strong SQL skills for data preparation, integration, and analysis. Experience preparing, integrating, and analyzing complex datasets from multiple systems. Experience with Databricks, Snowflake, or similar cloud data platforms for data preparation, feature engineering, scalable analytics, and reusable analytical workflow development. Familiarity with data engineering practices, including ETL/ELT, data modeling, pipeline development, and data quality checks. Experience moving analytical workflows beyond notebooks into reusable, maintainable, and governed solutions. Experience in supply chain, manufacturing, planning, logistics, clinical supply, life sciences, operations analytics, or related domains. Familiarity with modern application or web technologies such as React, Node.js, APIs, or cloud deployment patterns, particularly when used to deliver analytical or decision-support tools. Ability to partner with business stakeholders, frame analytical questions, translate needs into data product requirements, and communicate insights clearly. Strong documentation, version control, testing, validation, and reproducibility practices.

Soft Skills

Excellent problem-solving, storytelling, communication, and interpersonal skills, with the ability to explain technical concepts in clear business language. Strong verbal and written communication skills, with attention to detail, organization, documentation, and presentation abilities. Ability to bridge technical and non-technical teams, synthesize stakeholder inputs, and translate data and AI insights into meaningful business recommendations. Skilled in breaking down complex problems, documenting problem statements, estimating effort, assessing technology tradeoffs, and analyzing implementation impact. Independent, self-motivated, organized, and able to manage multiple priorities in fast-paced, time-sensitive environments. Strong collaboration, facilitation, and team-working skills, including experience working effectively with global virtual teams. Demonstrated ability to deliver results across Agile projects, adapt to setbacks, explore alternative approaches, and maintain persistence through completion. Awareness of industry trends, emerging technologies, and new approaches for solving complex business problems.