Senior Manager - Data Sciences & AI

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 Senior Manager - Data Sciences Artificial Intelligence to lead a team of data scientists responsible for building scalable digital, data, and AI-enabled solutions for clinical and commercial supply chain processes. This multi-faceted team leader will build and establish a new group within AIN that combines people leadership, product ownership, client success, data science, supply chain modelling and AI engineering.

This role will be accountable for shaping a high-performing local team while also driving the delivery of high-impact digital capabilities for Global Supply Chain. This is a unique opportunity to help build a new footprint at AIN from the ground up and strengthen an industry leading capability with a track record in the digital transformation space at the intersection of supply chain management, data, modeling, and artificial intelligence.

ROLES RESPONSIBILITIES

Responsibilities will include, but are not limited to:

• People leadership and line management: coaching, performance management, hiring input, workload prioritization, team health, and talent development.
• Technical leadership and cross-functional delivery: Guide data scientists, product owners, business partners, architecture teams, AI platform teams, data teams, and stakeholders through delivery decisions, dependencies, and tradeoffs.
• Product ownership, domain translation, and client success: Partner with subject-matter experts, business stakeholders, and technical teams to translate supply chain needs into scalable product solutions that deliver measurable outcomes.
• Data science and data engineering fluency: applying core principles of artificial intelligence, machine learning, data modeling, data quality, metadata, data lineage, and data pipelines to define solutions to complex problems.
• AI engineering and application delivery: building, integrating, evaluating, and operationalizing AI/ML capabilities, including context engineering, agents/workflows, evaluations, guardrails, and human-centric designs.
• Learning agility and hands-on technical credibility: demonstrating leading-edge understanding of AI, semantic technologies, and product practices while retaining enough technical depth to prototype, review designs, challenge assumptions, and unblock teams.
• Pragmatic prioritization and delivery management: balancing business needs, technical debt, delivery capacity, risk, speed, dependencies, stakeholder expectations, and long-term scalability across multiple projects.
• Enterprise architecture, quality, and compliance leadership: ensuring solutions are secure, reliable, maintainable, well-documented, and aligned with enterprise architecture, operational standards, cybersecurity, data privacy, model governance, GxP, HIPAA, and other applicable life sciences compliance expectations.
• Innovation and engineering culture: fostering a culture of innovation, accountability, inclusion, continuous learning, technical curiosity, rapid prototyping, platform-first thinking, and high-quality delivery.

FUNCTIONAL SKILLS

Must-Have Skills

• Experience in pharma, biotechnology, life sciences, regulated manufacturing, or GxP-compliant technology environments with quality, compliance, data privacy, security, and documentation expectations.
• Strong understanding of supply chain processes.
• Demonstrated experience leading software engineering, AI engineering, data engineering, analytics, or technical delivery teams.
• Experience operating in product, platform, data, analytics, or enterprise application environments.
• Ability to translate business needs into scalable, secure, maintainable, and compliant technology solutions.
• Experience operating in Agile delivery environments and managing multiple priorities, projects, and stakeholder groups.
• Experience supporting end-to-end software delivery from concept through production stabilization and ongoing support.
• Ability to assess technology options, understand tradeoffs, and make sound recommendations based on business value, cost, risk, compliance, technical debt, delivery capacity, and long-term impact.