Digital R&D Product Owner – Portfolio & Clinical AI Workflows

Digital Product Owner – Portfolio & Clinical AI Workflows

About the Role

We are seeking a Digital Product Owner to lead AI-driven workflows supporting Portfolio and Clinical research across R&D.

This role owns the design, development, and adoption of AI/ML-enabled workflows that accelerate portfolio decision-making, clinical data analysis, and cross-program insights. The position sits at the intersection of data science, clinical development, and digital platforms — translating complex scientific and operational needs into scalable, production-ready digital solutions.

You will work closely with clinical scientists, portfolio strategy teams, data scientists, and platform engineering to deliver AI-enabled workflows that are reliable, compliant, and measurable in impact. This is a domain-focused AI product ownership role built on top of enterprise workflow and data platforms.

Key Responsibilities

1. Product Ownership

• Own the roadmap and backlog for Portfolio & Clinical AI workflows.

• Define use cases and requirements for AI/ML-driven analytics, decision-support tools, and automated workflow capabilities.

• Translate scientific, clinical, and portfolio needs into clear product specifications.

• Ensure workflows are production-ready, compliant, and aligned with enterprise standards.

2. AI Workflow Design & Enablement

• Partner with data scientists to operationalize machine learning and generative AI models into reproducible workflows.

• Define integration patterns between AI models, enterprise data systems, and workflow execution environments.

• Ensure traceability, reproducibility, and auditability of AI-driven outputs in regulated contexts.

3. Stakeholder & Cross-Functional Collaboration

• Collaborate with:

  Clinical development teams

  Portfolio strategy and governance stakeholders

  Data science and AI engineering teams

  Platform and infrastructure partners

• Align workflow priorities with portfolio milestones and clinical development timelines.

• Proactively manage dependencies on underlying data and workflow platforms.

4. Delivery & Execution

• Lead agile development cycles in partnership with engineering and Scrum teams.

• Maintain a prioritized backlog that balances short-term impact with long-term workflow scalability.

• Define success metrics tied to adoption, efficiency gains, and scientific outcomes.

• Oversee pilot launches, iteration cycles, and scaling of successful workflows.

5. Continuous Improvement & Impact Measurement

• Track adoption, usability, and scientific impact of deployed AI workflows.

• Gather feedback from clinical and portfolio users to refine capabilities.

• Identify new AI opportunities aligned with strategic research priorities.

Key Requirements

Experience

• 5+ years of experience in Product Management or Product Ownership, preferably in life sciences, clinical research, or AI/ML environments.

• Experience delivering AI-driven or analytics-driven products to scientific or clinical users.

• Strong understanding of clinical development processes and portfolio governance frameworks.

• Familiarity with cloud platforms (e.g., AWS) and enterprise data systems.

Technical Skills

• Solid understanding of machine learning lifecycle concepts (model development, validation, deployment, monitoring).

• Experience translating data science outputs into operational workflows.

• Familiarity with data engineering, APIs, and workflow orchestration environments is a plus.

• Understanding of compliance and audit requirements in regulated research settings is preferred.

Soft Skills

• Strong ability to communicate complex AI concepts to non-technical stakeholders.

• Strategic thinking with a focus on measurable research and business impact.

• Proven ability to operate across scientific, technical, and operational domains.

• Comfortable managing ambiguity in emerging AI use cases.

Education

• Bachelor’s or Master’s degree in Data Science, Bioinformatics, Computer Science, Clinical Research, or related field.

• Advanced degree (Ph.D., M.D., or equivalent) in a scientific or clinical discipline is a plus.