Digital Product Mgr-AI&D
Career Category
Join Amgen’s Mission of Serving Patients
Digital Product Manager (AI)
Role Overview
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.
Basic Qualifications:
- 8-13 years of work experience in the related field
- Doctorate; or Master's degree and 2 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience; or
- Bachelor's degree and 4 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience; or
- Associate's degree and 8 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience; or
- High School Diploma or GED and 10 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience.
Preferred Qualifications
- 4+ years of product management, product strategy, innovation, consulting, digital transformation, or related experience.
- 2+ years of experience with AI, machine learning, data products, automation, LLMs, agents, NLP, analytics, or enterprise AI solutions.
- Working knowledge of
AI product development
including discovery, prototyping, model evaluation, deployment, monitoring, and iteration.
- Familiarity with generative AI, LLMs, retrieval-augmented generation, AI agents, prompt engineering, model orchestration, NLP, predictive analytics, or intelligent automation.
- Design thinking experience
- Ability to evaluate AI opportunities across desirability, feasibility, viability, value, risk, scalability, and data readiness.
- Experience with AI platforms, MLOps, or enterprise AI architecture.
- Familiarity with AI (e.g., LLMs, agents, NLP) and ML productization at scale.
- Strong presentation and public speaking skills with experience communicating to executives
- Familiarity with biotech workflows (e.g., lab automation, computational biology, clinical data, etc).
- Hands-on ability to analyze product usage, model output quality, or design A/B experiments.
- Comfort working in fast-paced, cross-functional environments where priorities evolve quickly.
- Strong stakeholder management skills, including the ability to build relationships and collaborate effectively across functions and levels.
- Consulting experience, with demonstrated ability to structure and solve complex business problems.
- Experience conducting cross-industry landscape assessments to identify how AI is being leveraged to address shared problem spaces and uncover macro-level trends and opportunities
.
Not included in the source posting: benefits.
Skills
Who can apply
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