Head of Data Strategy

 The Head of Data Strategy will bea key seniorleader within the USDigital, Insights& Analytics community, responsibleforbuildingandleadingthe data strategycapabilitythat enables trusted, scalable, and AI-ready commercialization decision-making. Thisrole will define howcommercialization data is acquired, managed, governed, organized, and activated through high-value data products in close partnership with DT (Digital Technology), the global insights community, and senior business stakeholders across the organization. The role will also connect data strategy to meaningful commercialization outcomes, including improved patient pull-through, time-to-therapy, and overall performance.

Key Accountabilities/Responsibilities:

Lead thecommercializationdata strategy capabilityto support growth acrossthe portfolio, includingthe data acquisition roadmap,governance model, data product strategy, operating rhythms, and enterprise standards required to drive scalable analytics, AI readiness, and commercialization performance.

• Partner across the commercialization data ecosystem to strengthen data acquisition, data product management, governance, data quality, business enablement, and adoption.

• Define commercialization data architecture, platform requirements, integration priorities, master dataneeds, data quality controls, and AI-enablementcapabilities in close partnershipwith DT (Digital Technology) and the globalinsights community.

• Providetargeted support to the globalinsights community andglobal teams on data strategy, data acquisition, andcapability building, helpingalign approaches, share reusablestandardsandsolutions, andstrengthen data foundations acrossmarkets.

• Lead commercialization data acquisition strategy, including identification, evaluation, onboarding, rationalization, and lifecycle management of external and internal data sources to reduce duplication, improve value from data investments, and support analytics, reporting, segmentation, forecasting, AI, and data product development.

• Establishand maintain commercializationdata governance practices, including definitions, ownership, stewardship, quality standards, metadata management, access rules, privacy and compliance considerations, issue-resolution processes, and mechanisms to identify and prevent duplicative or inconsistent data acquisition.

• Create and manage a portfolio of commercialization data products that translate data into actionable insights and reusablecapabilities supporting field execution, access strategy, self-serve analytics, AI-enabled use cases, andcommercialization decision-making.

• Advance AI readiness byensuring data assets are well-governed, high-quality, discoverable, appropriately documented, interoperable, and fit for analytical, automation, and AI-enabled workflows.

• Translate commercialization business needs into clear data requirements, product backlogs, delivery priorities, and adoption plans in partnership withCommercial Analytics & Insights, DT, and functional stakeholders.

• Partneracross commercialization communities, including patient, HCP, market access, and medical affairs evidence generation stakeholders, to align data standards, expectations, source strategies, and requirements needed to enable consistent insight generation.

• Support data fluency and adoption acrosscommercializationteams by improving data usability, clarity, consistency, and accessibility, whilehelping users understand trusted data sources, definitions, and appropriateuse casesacross commercialization workflows.

• Manage key data vendorand partner relationships, including data acquisition agreements, data quality expectations, service levels, documentation, and ongoing performance management.

• Define and monitor measures of data value, quality, completeness, timeliness, usage, adoption, and readiness for advanced analytics and AI-enabled applications.

• Collaborate with Commercial Analytics & Insights, DT, and business stakeholders to prioritize data and analytics investments based on commercialization priorities, AI readiness needs, feasibility, risk, and expected business impact.

• Serve as the senior data strategy subject matter expert and trustedthought partner tocommercialization leaders, shapingthe data foundation required for launch planning, in-market performance management, pipeline planning, and future digital and AI capabilities.

• Partner withFinance, Procurement, Legal, Compliance, and DT to support data contracts, data rights, privacy considerations, and budget management for commercialization data assets and platforms.

Desired Skills and Experience:

Deep expertise incommercialization data strategy, data acquisition, data management, governance, data architecture, data products, and analytics enablement within the biopharmaceutical industry, with a track record of setting direction across complex organizations.

• Demonstrated ability to partner with DT (Digital Technology) and business stakeholders to translate data needs into scalable platform, integration,governance, and AI-readiness requirements.

• Experience building reusable data products, data marts,curated datasets, semantic layers, orother trusted data assets that support analytics, reporting, self-serve insights, and AI-enabled workflows.

• Deep appreciation for data quality, metadata, lineage, stewardship, access management, compliance, privacy, and the governancefoundations required forresponsible analytics and AI use.

• Proven ability to assess, onboard, manage, and optimize external data vendors, data providers, and implementation partners.

Executive-level communication, facilitation, and stakeholderleadership skills, with the judgment and credibilityto influence acrosscommercialization communities, DT, Finance, Legal, Compliance, and external partners.

• Abilitytowork in a hands-on, builder environment whileestablishingstructure, standards, andscalableways of workingfor a growing data strategy function.

Strong enterprisebusiness judgment and ability to makeand guide investment trade-offs based on strategic value, feasibility, risk, data rights, AI readiness, and expected impact on commercialization performance.

• Ability to drive data fluency, adoption, and behavior change across business teams by making data products easier to understand, trust, and use in day-to-daydecision-making.

• Good understanding of pharmaceuticalcommercialization, data privacy, compliance, regulatory considerations, and appropriate use of healthcare and commercial data.

Education and Qualifications:

• BA or BS required, MBA Preferred

• Minimum of 12-15 years of experience in data strategy, data management, commercializationoperations, analytics enablement, business intelligence, or related functions within the biopharmaceutical, healthcare, or life sciences industry.

• Demonstrated experience defining and leading enterprise-scale data governance, data acquisition, data product, data platform, data quality, or AI-readiness initiatives in partnership with technology teams.

• Strong analytical and systems-thinking skills, with the abilityto connect business questions, data assets, platform capabilities, governance requirements, and end-user adoption.

• Start-up, launch, or capability-building experience preferred.

• Familiaritywithleadinghealthcareandpharmaceutical data sources, data providers, analytics platforms, cloud data environments, and data governance tools preferred.

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