Senior Manager - Data Sciences & AI

Job description

Career CategorySupply ChainJob DescriptionABOUT 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 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. .