R-256479- Digital Product Manager (AI)

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Digital Product Manager (AI) The Digital Product Manager at Amgen oversees the lifecycle of digital products, working closely with various teams to define the product vision, roadmap, and growth opportunities.

Role Overview We are seeking a Digital Product Manager, to join the AI & Data Innovation Lab Product Management Team. This role is focused on rapidly validating high-potential AI and data-driven business opportunities before major investment, helping Amgen determine which AI initiatives should be scaled, refined, paused, or redirected.

Operating at the intersection of business strategy, AI product innovation, data, technology, and enterprise transformation, this role will provide strategic product leadership across ambiguous, cross-functional opportunities and influence senior business and technology stakeholders on product direction, prioritization, investment, and scale.

This role requires a strong product mindset, sufficient technical depth to evaluate whether AI and data opportunities are practical, scalable, measurable, and aligned to Amgen’s enterprise technology direction. The successful candidate will understand how AI products are built, validated, deployed, monitored, and scaled in complex enterprise environments.

What You’ll Do

• Design and execute rapid discovery sprints, prototypes, pilots, user research, and experiments to assess desirability, feasibility, viability, value, and risk. Use evidence to determine whether opportunities should advance, pivot, pause, or scale.
• Identify, frame, and validate AI-enabled business opportunities across Amgen. Translate broad transformation themes into clear problem statements, testable hypotheses, user needs, data requirements, success metrics, and validation plans.
• Assess how LLMs, AI agents, machine learning models, NLP, automation, knowledge retrieval, analytics, and intelligent workflow tools can improve productivity, decision quality, scientific discovery, operational efficiency, and workforce effectiveness.
• Evaluate whether proposed AI opportunities have the right data foundations, system integrations, architecture patterns, model capabilities, security controls, and operational support needed to move from concept to scalable solution.
• Partner with engineering, data science, and technical teams to align on scope, tradeoffs, and execution path.
• Conduct landscape assessments across different industries to learn how AI is leveraged to solve shared problem spaces at the macro level.
• Develop product requirements that account for user experience, data availability, model performance, output quality, explainability, reliability, latency, integration needs, governance, and ongoing measurement.
• Synthesize findings from validation work into clear product recommendations, opportunity briefs, technical feasibility assessments, business cases, and executive-ready narratives. Help leaders make informed decisions about where to invest, scale, or stop.
• Ensure validation efforts consider responsible AI, data privacy, model risk, cybersecurity, compliance, regulatory considerations, human oversight, change management, and enterprise scalability from the earliest stages of discovery.
• Facilitate workshops, discovery sessions, prioritization discussions, technical feasibility reviews, user experience design, process mapping, and decision forums. Communicate complex AI and data concepts in a clear, business-oriented way for senior leaders and non-technical stakeholders.

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