Key Responsibilities

Commercial Data Product Strategy & Roadmap

• Own the commercial data product strategy aligned to enterprise commercial priorities (growth, mix, margin, retention, productivity), ensuring investments tie to measurable business outcomes.

• Define and manage a portfolio of business-aligned data productsand data solutionsacross core commercial domains(e.g.,executiveand commercial analytics, revenue generation, seller productivity, pipeline and opportunity management,territoryand account potential).

• Establish and run a data product lifecycle (intake, discovery, prioritization, release planning, adoption, value measurement, retirement),including versioning and lifecycle governance for core business logic.

• Build andmaintainmulti-horizon roadmaps (near-term operational wins + mid/long-term strategic capabilities) with clear sequencing and dependencies across platforms, engineering, and AI.

• Drive a single global prioritization mechanism that balances BU/Region needs with enterprise standardizationand ensures ownership/alignment across all key stakeholders.
  

Value Realization & Adoption

• Define clear value cases and success metrics for each dataproduct and data solution(e.g., win rate lift, cycle-time reduction, forecast accuracy, rep productivity).

• Partner with commercial leaders to embed products into core workflows (CRM, CPQ, service, marketing automation), ensuring tools are adopted and operationalized—not just delivered.

• Establishadoptioninstrumentation and benefits tracking, creating transparency into usage, impact, and opportunities for iteration.

• Create repeatable playbooks for product rollout, enablement, and change management across Regions and Business Units.
  

AI-Driven Product Approach

• Define and own AI‑enabled commercial dataproductsand data solutions(e.g.,opportunityhealth scoring, forecasting intelligence, next best action) as part of the enterprise dataproductportfolio.

• Translate commercial priorities into clear AI product use cases, success metrics, and adoption expectations.

• Partner with Commercial Decision Science & AI to ensureAImodels are aligned to real commercial workflows and decision moments.

• Ensure AI capabilities are embedded into seller and leader workflows (CRM, CPQ,CLM) to drive adoption and measurable impact.

• Continuously evaluate emerging AI use cases from a business-value perspective and incorporate them into data productroadmapswhere they deliver incrementaloutcome.
  

Business Taxonomy, Metrics, and Semantic Alignment

• Own the commercial business taxonomy (domains, entities, hierarchies) and ensure semantic consistency across dashboards, models, and executive reporting.

• Lead definition and stewardship of enterprise commercial metrics and business logic (pipeline, stage definitions, conversion, forecast categories, pricing metrics, account coverage, omnichannel engagement).

• Stand up and govern a semantic alignment process so “one metric” means the same thing everywhere, with traceability to source systems and governance policies.

• Partner closely with Analytics Engineering to ensure semantic definitions are translated into reusable, governed semanticlayersand standardized consumption patterns.
  

Leadership, Stewardship& Global Standardization

• Lead and develop Commercial Data Product Management Leads for core domains, ensuring clear ownership, consistent taxonomy and metric definitions, and measurable value realization across Business Units and Regions.

• Build and lead business stewardship networks across markets and functions to drive alignment, resolve definition conflicts, and scale best practices.

• Establish a scalable operating model for global standards with local flexibility (e.g., core metric standards +permittedregional extensions).

• Serve as the primary commercial-facing leader for data product decisions, influencing stakeholders across a matrixed, global enterprise.

Qualifications

• 10+ years of progressive experience in commercial analytics, product management,commercialoperations, or related domains within MedTech / Life Sciences / regulated industries (preferred).

• 5+ years in leadership roles driving cross-functional programs with enterprise-wide adoption and measurable impact.

• Proven ability to define and scale dataproducts/ data solutionsfrom concept to global rollout, including roadmaps, lifecycle governance, and value realization.

• Strong understanding of commercial domains and processes: pipeline/opportunity management, forecasting, territory/account planning, pricing/CPQ, customer segmentation, omnichannel engagement.

• Demonstrated experience defining and governing business taxonomies, KPI frameworks, and semantic layers in partnership with engineering andanalyticsteams.

• Strong stakeholder leadership: ability to influence Sales, Marketing, Finance, IT/GBS, BU and Regionalleadersin a matrixed organization.

• Executive-ready communicator withabilityto synthesize complexity into clear decisions, tradeoffs, and recommendations.
  

Education:Bachelor’s degreerequired(Business, Economics, Engineering, Data/Analytics, or related). MBA/MSor advanced degree preferred.

Desired Skills & Experience

• Experience working with commercial platforms such as Salesforce, data visualization tooling (e.g., Power BI), and modern data environments (cloud/lakehouseconcepts).

• Familiarity with data governance concepts (metadata, lineage, quality controls, access/privacy) and how they connect to product delivery.

• Experience partnering with advanced analytics / data science teams and understanding how feature/label definitions and semantic consistency impact model performance and trust.

• Experience in global operating models spanning multiple Business Units/Regions and leadingchangeacross diverse markets.

• Comfortoperatingin ambiguity; ability to build structure, standards, and repeatable mechanisms without slowing execution.

Leadership Attributes

• Value-led productthinker:prioritizes outcomes and adoption over outputs and technovelty.

• AI‑literatestrategistwho understands where and how AI creates commercial advantage without over‑engineering or chasing hype.

• Enterprise mindset: drives standardization while balancing BU/Region realities and time-to-value.

• Strong operator: builds durable operating mechanisms (intake, prioritization, governance, lifecycle, adoption tracking).

• Changeleader:brings stakeholders along, shapes behaviors, and accelerates uptake of new standards and ways of working.

• Clear, concise communicator: confident presenting to senior leadership; able to drive crisp decisions and alignment.

• High integrity, resilience, learning agility, anda biasfor action.