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Amgen Logo Amgen · IT - Software Development

Senior Manager - Data Sciences & Artificial Intelligence

India, Hyderabad · Job Posted June 04, 2026
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Job Description

Transformative Digital Capabilities (TDC) drives Process Development (PD) and Operations digital transformation through pragmatic digital innovation that systematically delivers for the present while also building for the future. Our mission is to empower Process Development staff to harness their transformative potential through the rapid convergence of chemistry, biology, engineering, and computing, accelerated by innovative digital capabilities. TDC’s core competencies span clear thought leadership and deep domain expertise in Data Products, Artificial Intelligence, Digital Twins, Product Management, and Organizational Change Management following Scaled Agile practices. As part of our team expansion at Amgen India (AIN), we are seeking a multi-faceted team leader to build and establish a new group within AIN that combines people leadership, product ownership, client success, data science, ontology and knowledge graph expertise, in silico modeling, and AI engineering.

Job Responsibility

  • Accountable for shaping a high-performing local team while also driving the delivery of high-impact digital capabilities for Process Development
  • Partner closely with an established team operating at the cutting edge of Data/Twins, AI, and Product Management, bringing the leadership judgment, technical credibility, and product mindset needed to translate complex scientific and business needs into scalable solutions
  • 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 science/engineering, data, modeling, and artificial intelligence

Requirements

  • People leadership and line management: coaching, performance management, hiring input, workload prioritization, team health, and talent development
  • Technical leadership and cross-functional collaboration: guiding data scientists, AI engineers, ontology specialists, product owners, business partners, and stakeholders through delivery decisions, dependencies, and tradeoffs
  • Product ownership and client success: defining product vision, roadmap, backlog, prioritization, release planning, stakeholder expectations, feedback loops, adoption strategy, and measurable client outcomes
  • Strategic problem framing: converting ambiguous business, scientific/engineering, or operational needs into actionable product, data, and AI use cases
  • Ontology and knowledge graph implementation: defining and implementing controlled vocabularies, taxonomies, semantic models, entity relationships, graph data models, entity resolution approaches, and semantic query patterns
  • Data science and data engineering fluency: applying core principles of machine learning, in silico modeling, data modeling, data quality, metadata, data lineage, and data pipelines to define the solution to complex problems
  • AI engineering and application delivery: building, integrating, evaluating, and operationalizing AI/ML capabilities, including retrieval-augmented generation, context engineering, agents/workflows, evaluations, guardrails, and human-centric designs
  • Scientific or enterprise domain translation: bridging subject-matter experts, business stakeholders, and technical teams to ensure the product solves real problems in context and with extreme agility
  • Learning agility and hands-on technical credibility: demonstrating leading-edge understanding and command of AI, semantic technologies, and product practices while retaining enough depth to prototype, review designs, challenge assumptions, and unblock teams
  • Pragmatic prioritization and tradeoff management: balancing client needs, technical debt, delivery capacity, risk, speed, and long-term product scalability
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