Head of Data Strategy

argenx

1d•$236,000 - $324,500•Remote

About The Position

Join us as we transform immunology and deliver medicines that help autoimmune patients get their lives back. argenx is preparing for multi-dimensional expansion to reach more patients through a rich pipeline of differentiated assets, led by VYVGART, our first-in-class neonatal Fc receptor blocker approved for the treatment of gMG, and with the potential to treat patients across dozens of severe autoimmune diseases. We are building a new kind of biotech company, one that maintains its roots as a science-based start-up and pushes our commitment to innovate across all corners of our business. We strive to inspire and grow our company, our partnerships, our science, and our people, because when we do, we deliver more for patients. The Head of Data Strategy will be a key senior leader within the US Digital, Insights & Analytics community, responsible for building and leading the data strategy capability that enables trusted, scalable, and AI-ready commercialization decision-making. This role will define how commercialization 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.

Requirements

Deep expertise in commercialization 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, or other 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 governance foundations required for responsible 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 stakeholder leadership skills, with the judgment and credibility to influence across commercialization communities, DT, Finance, Legal, Compliance, and external partners.
Ability to work in a hands-on, builder environment while establishing structure, standards, and scalable ways of working for a growing data strategy function.
Strong enterprise business judgment and ability to make and 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-day decision-making.
Good understanding of pharmaceutical commercialization, data privacy, compliance, regulatory considerations, and appropriate use of healthcare and commercial data.
BA or BS required, MBA Preferred
Minimum of 12-15 years of experience in data strategy, data management, commercialization operations, 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 ability to connect business questions, data assets, platform capabilities, governance requirements, and end-user adoption.

Nice To Haves

Start-up, launch, or capability-building experience preferred.
Familiarity with leading healthcare and pharmaceutical data sources, data providers, analytics platforms, cloud data environments, and data governance tools preferred.

Responsibilities

Lead the commercialization data strategy capability to support growth across the portfolio, including the 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 data needs, data quality controls, and AI-enablement capabilities in close partnership with DT (Digital Technology) and the global insights community.
Provide targeted support to the global insights community and global teams on data strategy, data acquisition, and capability building, helping align approaches, share reusable standards and solutions, and strengthen data foundations across markets.
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.
Establish and maintain commercialization data 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 reusable capabilities supporting field execution, access strategy, self-serve analytics, AI-enabled use cases, and commercialization decision-making.
Advance AI readiness by ensuring 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 with Commercial Analytics & Insights, DT, and functional stakeholders.
Partner across 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 across commercialization teams by improving data usability, clarity, consistency, and accessibility, while helping users understand trusted data sources, definitions, and appropriate use cases across commercialization workflows.
Manage key data vendor and 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 trusted thought partner to commercialization leaders, shaping the data foundation required for launch planning, in-market performance management, pipeline planning, and future digital and AI capabilities.
Partner with Finance, Procurement, Legal, Compliance, and DT to support data contracts, data rights, privacy considerations, and budget management for commercialization data assets and platforms.