Career Category

Job Description

Obesity Intelligence & Analytics | Amgen India

Reports to: Executive Director, Obesity Intelligence & Analytics

Role Summary

This is an India-based global leadership role supporting Amgen's global obesity business. This role is about making intelligence easier to build, trust, scale, and use.

The Director, Technology, Operations & Data Enablement will lead the platforms, tools, information assets, AI/ML enablement, and delivery practices that allow Obesity Intelligence & Analytics to move faster while maintaining the same level of quality and trust. It includes business-facing tools, reusable decision products, reliable information pipelines, and operating processes that help teams turn complex questions into action.

Success requires understanding how leaders make decisions, where work slows down, and which capabilities are worth scaling. The Director will build an India-based team, partner globally, and create the capabilities, products, and operating practices that help the organization answer increasingly complex business questions with greater speed, consistency, and confidence.

Key Responsibilities

1. Technology, Operations & Data Enablement Strategy

• Set the enablement agenda for the obesity intelligence ecosystem, connecting business priorities with the platforms, tools, AI/ML capabilities, and information assets required to support them.
• Build roadmaps that make trade-offs visible: what to automate, what to standardize, what to productize, and what should remain bespoke.
• Separate useful innovation from distraction by evaluating emerging technologies, analytical methods, and industry practices through the lens of adoption, scalability, risk, and business value.
• Shape a future-state environment that supports increasingly sophisticated intelligence needs while staying aligned with enterprise architecture, security, privacy, and responsible-use expectations.
• Bring structure to ambiguity. Translate broad stakeholder ambition into sequenced, practical capability-building plans.

2. Tools, Products, Platforms & Solutions

• Own delivery of business-facing tools and decision-support products that make insight generation more repeatable, intuitive, and scalable.
• Translate user needs into clear product requirements, release plans, adoption measures, and value stories.
• Lead development and enhancement of internal capabilities, scenario engines, intelligence portals, simulation tools, and other solutions that support decision-making.
• Create product management discipline without unnecessary bureaucracy: clear backlogs, crisp prioritization, better user experience, and visible adoption metrics.
• Work with technology and engineering partners to ensure solutions are reliable, secure, supportable, and integrated with enterprise standards.

3. Data Ecosystem & Reusable Assets

• Make information usable. Partner with enterprise data team to build an ecosystem that allows teams to find, understand, connect, and activate internal and external sources with confidence.
• Ensure data assets are acquired, integrated, governed, documented, quality-checked, and maintained across priority obesity use cases.
• Influence enterprise standards for stewardship, metadata, lineage, controls, and issue resolution so users trust what they are working with.
• Promote reusable assets, semantic layers, and shared components that reduce manual effort and prevent every project from starting from scratch.

4. AI, Machine Learning & Advanced Methods

• Move AI/ML from experimentation into responsible business use. Focus on solutions that improve decisions, reduce friction, or create meaningful efficiency.
• Enable predictive methods, optimization, automation, simulation, and applied AI capabilities that can be reused across multiple intelligence domains.
• Partner with data scientists, domain leaders, and technical teams to develop, validate, deploy, monitor, and improve models in real workflows.
• Create practical standards for model transparency, documentation, performance monitoring, and human oversight.
• Encourage experimentation, but insist on usefulness. The goal is not more models; it is better decisions and better adoption.

5. Enablement Operating Model & Delivery Excellence

• Define how work moves. Own the enablement operating model for intake, prioritization, release management, standards, vendor coordination, documentation, and adoption support.
• Create a delivery rhythm that is transparent enough for stakeholders, disciplined enough for scale, and flexible enough for a fast-moving business.
• Standardize reusable methods, components, templates, and development practices where consistency improves speed or quality.
• Manage external partners and capability investments with clear expectations for value, delivery, knowledge transfer, and long-term maintainability.
• Simplify the system. Remove avoidable friction, reduce redundant work, and help teams spend more time solving important problems.

6. Team Leadership & Talent Development

• Build and lead an India-based team across product management, operations enablement, data strategy, advanced methods, and technical delivery.
• Create a culture of ownership. High standards, practical innovation, inclusion, learning, and accountability should show up in the work, not just in team language.
• Develop future leaders who can move between business questions and technical choices with confidence.
• Set clear goals, decision rights, performance expectations, and career pathways that support both the organization and the individual.
• Inspire the team to think beyond capability delivery and focus on adoption, responsible design, stakeholder trust, and measurable value.

Basic Qualifications

• Doctorate degree OR Master's degree and OR Bachelor's degree and 18+ years of relevant experience.

Preferred Qualifications

• Advanced degree in Data Science, Computer Science, Engineering, Analytics, Information Systems, Business, Life Sciences, or a related field.
• Experience in technology, operations enablement, product management, data strategy, advanced analytics, digital transformation, or related functions within pharmaceuticals, biotechnology, healthcare, or life sciences.
• Demonstrated success building platforms, decision-support tools, reusable information assets, AI-enabled capabilities, or enterprise-grade solutions.
• Strong understanding of data management, cloud environments, product-based delivery, AI/ML applications, responsible AI practices, governance, and user adoption.
• Ability to translate broad business needs into practical products, operating processes, investment choices, and delivery plans.
• Experience establishing operating models, release practices, vendor routines, quality standards, and adoption mechanisms in complex global organizations.
• Proven ability to influence cross-functional stakeholders, including business, technical, privacy, security, architecture, and external partner teams.
• Comfort working in a global capability center model with India-based teams, distributed stakeholders, and evolving demand.
• Strong executive communication, strategic thinking, product judgment, vendor management, and ability to simplify technical complexity for business audiences.