Senior Manager, R&D Product Manager

BeiGene

3d•$141,700 - $186,700•Onsite

About The Position

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer. The Senior Manager, R&D Product Manager is responsible for designing, implementing, and scaling governance capabilities that enable trusted, compliant, and reusable clinical data across the R&D ecosystem. The Product Manager partners with clinical stakeholders, data stewards, IT teams, and analytics groups to implement data governance frameworks, data quality programs, metadata management, master/reference data strategies, and AI-ready data foundations. This role requires strong expertise in clinical data domains, governance processes, product development & lifecycle management, stewardship models, and modern data platforms supporting Clinical Development.

Requirements

Bachelor’s degree in Life Sciences, Data Management, Information Systems, or related field
7+ years’ experience in data stewardship, governance, or data management; ideally within Pharma R&D
Proven track record defining and operationalizing governance frameworks and leading cross-functional teams
Deep expertise in R&D data domains, FAIR principles, relevant regulatory requirements and data standards (CDISC, SDTM)
Hands-on experience leveraging data governance and management platforms (e.g. Informatica CDGC for cataloging and lineage, Informatica CDQ for data quality, Informatica marketplace, Reltio for master data management, Databricks for data product development and sharing, and tools for policy-driven access controls)
Strong leadership, mentoring, negotiation, and stakeholder-management skills
Excellent analytical, problem-solving, project-management, and communication abilities
Ability to champion data-driven culture and demonstrate the business value of governed data products

Nice To Haves

Microsoft Applications (Outlook, Word, Excel, PowerPoint, Project)

Responsibilities

Drive governance and data product strategies across critical clinical domains including program management, clinical operations, data management, financial governance, resource management and operational metrics.
Translate governance standards into measurable business outcomes, define target KPIs (e.g., reduced time-to-insight, improved decision accuracy), align with R&D objectives, and drive execution roadmaps.
Define and maintain the product vision, roadmap, and capability maturity model for R&D data governance and data products.
Manage product backlog, prioritize features, and coordinate releases in collaboration with engineering and platform teams.
Provide leadership to cross-functional teams including data stewards, engineers, and analysts in a matrix operating model.
Drive deployment of data governance tools (metadata catalog, data lineage, access controls) and integrate them into the data products delivered.
Develop user enablement materials such as training modules, quick-reference guides, and drop-in support sessions, to accelerate self-service adoption.
Partner with R&D business owners to define and deliver trusted, reusable and governed data products that support strategic insights and operational efficiency.
Establish data product SLAs, usage metrics, and feedback loops to continuously enhance data product value and adoption.
Monitor data quality metrics (completeness, accuracy, timeliness) in partnership with data engineering; coordinate remediation plans.
Partner with compliance teams to ensure alignment with GxP, CSA, and regulatory inspection readiness.
Collaborate with teams supporting R&D platforms including EDC, CTMS, IRT, eTMF, and enterprise data platforms to translate functional requirements into governed data pipelines and trusted data products.
Escalate emerging data risks and propose operational enhancements to minimize downstream impact.
Ensure governance frameworks support responsible AI, including metadata traceability, lineage, and model transparency.
Enable high-quality curated datasets that support AI/ML use cases such as patient recruitment prediction, site performance analytics, and trial risk detection.
Collect user feedback and governance health-check results to identify process optimizations.
Track and report on data-product adoption, data-product usage, and business value metrics (e.g., time saved, decision-cycle reduction) against established targets.