Director of Data & AI Products

AbbVie•Mettawa, IL

1d•$182,000 - $346,000•Onsite

About The Position

As Head of Data and AI Technical Product Management, this leader serves as the global Commercial Data & AI technical product leader accountable for the operating model, portfolio strategy, investment trade-offs, product lifecycle, adoption, and product talent buildout for a large, regulated, global commercial data and AI ecosystem. The role builds and scales AbbVie’s Technical Data Product Ownership practice, serving as the IT counterpart to Commercial Business Product Owners and defining how technical data and AI products are discovered, prioritized, funded, built, launched, adopted, supported, measured, modernized, and retired. This leader translates Commercial product roadmaps and data consumer needs into durable, measurable technical product roadmaps while partnering with architecture, engineering, security, privacy, governance, analytics, and business teams. The role establishes the standards, lifecycle discipline, decision rights, adoption practices, funding mechanisms, and performance scorecards needed to move Global Commercial Data and AI from project-based delivery to product-based ownership at enterprise scale.

Requirements

Bachelor’s Degree plus 12+ years of progressive experience in technical product management, platform leadership, data and analytics, AI-enabled products, or a related discipline, including global portfolio ownership, operating model leadership, investment accountability, people leadership, and senior accountability for measurable outcomes in a complex enterprise environment.
Demonstrated ability to define multi-year strategy, secure alignment with executive stakeholders, and translate business objectives into funded roadmaps and measurable value realization.
Developed, launched, operated, and improved multiple production data products and AI-enabled capabilities at enterprise scale, with demonstrated discipline across discovery, lifecycle management, reliability, adoption, governance, and value realization.
Experience leading a shift from project-based delivery to product-based operating models in a low-maturity or evolving organization, including role definition, intake, prioritization, roadmap standards, lifecycle management, product metrics, adoption practices, and change enablement.
Proven product talent buildout and people leadership, including building or scaling teams of Product Managers and/or product leaders, hiring, coaching, performance management, succession planning, communities of practice, and product management standards.
Demonstrated portfolio investment ownership, including annual OPEX/CAPEX planning, funding recommendations, capacity allocation, vendor/partner spend, cost-to-serve optimization, modernization trade-offs, and linkage of investment decisions to value, adoption, reliability, and risk outcomes.
Deep understanding of modern data ecosystems and operating models: ETL/ELT, data warehouses/lakes, APIs/event streams, metadata/lineage, access controls, and governance at scale.
Proven leadership partnering with engineering, architecture, security/privacy, and governance to drive quality, reliability (SLAs/SLOs), compliance-by-design, and platform modernization.
Exceptional executive communication, influence, and prioritization skills; able to lead steering forums, resolve cross-domain trade-offs, and drive adoption and change across large organizations.
Technology fluency expected: deep fluency across modern cloud, data, analytics, AI, metadata, lineage, catalog, governance, API, eventing, orchestration, and platform reliability patterns. Experience with specific tools such as AWS, Azure, GCP, Snowflake, Iceberg, Airflow, dbt, Alation, SageMaker, Microsoft AI capabilities, Anthropic, agentic platforms, SQL, Python concepts, APIs, and AI/BI tools is illustrative and helpful, but transferable technical product leadership across comparable ecosystems is most important.

Nice To Haves

Experience in regulated industries, with life sciences/pharma Commercial data and analytics preferred, including sales, marketing, market access, omnichannel, CRM, and associated regulatory/privacy considerations.
Experience building enterprise data/AI platforms and governance operating models (data catalog, data mesh/fabric concepts, stewardship, policy enforcement) across multiple business units and geographies.
Experience managing complex vendor ecosystems and strategic partners (RFPs, contracting, SLAs, performance management), including multi-year platform programs and large-scale change management

Responsibilities

Serve as the global Commercial Data & AI technical product leader, owning the product operating model, multi-year portfolio strategy, investment trade-offs, lifecycle discipline, adoption strategy, and product talent buildout for a large, regulated, global commercial data and AI ecosystem.
Lead the technical product roadmap across data products, AI-enabling capabilities, platform services, and self-service experiences aligned to Commercial priorities and enterprise risk expectations.
Own a multi-million-dollar annual Commercial Data & AI technical product portfolio supporting 30 commercial data domains, 30+ AI products and platform capabilities, 40+ brands/business units, and 120 countries.
Build and scale a direct team of Technical Product Managers and product leaders, with intended responsibility for 10–20 product professionals and influence across 50–100+ matrixed engineering, AI, architecture, governance, analytics, and vendor resources as the organization matures.
Own the Technical Data Product Management strategy, operating model, and product lifecycle for Commercial Data and AI capabilities, with accountability from discovery and roadmap integration through launch, adoption, support, versioning, modernization, and retirement.
Partner with enterprise architecture, engineering, security, privacy, and governance leaders to make product trade-offs and ensure technical decisions align to business value, risk posture, and platform strategy.
Accountable for product strategy, prioritization, roadmap trade-offs, adoption goals, lifecycle decisions, and value/risk recommendations for technical data and AI products.
Define decision rights and working models with Commercial Business Product Owners and partners with architecture, engineering, security, privacy, and governance leaders on technical standards, risk acceptance, and compliance-by-design.
Drive Global Commercial Data & AI technical product strategy across 40+ brands/business units, 120 countries, and hundreds of businesses, analytics, technology, and governance stakeholders.
Partner with adjacent enterprise functions where Commercial data and AI products intersect, while maintaining clear primary accountability for the Commercial technical product ecosystem.
Own the annual engineering and technical portfolio budget and investment model; develop technical business cases, allocate funding across engineering initiatives, manage compute/cost-to-serve, and ensure vendor/partner spend aligns to product outcomes, architectural direction, and risk posture.
Build and scale the Technical Data Product Management organization and talent bench, including hiring, coaching, performance management, succession planning, role standards, playbooks, communities of practice, and product leadership capability across direct and matrixed teams.
Serve hundreds of business, analytics, technology, and governance stakeholders globally; accountable for measurable technology outcomes such as reduced data latency, system uptime, engineering productivity gains, and compliance posture through improved data/AI reliability, discoverability, and adoption.
Operates at executive level, chairing or co-chairing technical steering committees and governance forums and influencing VP/SVP stakeholders through technical narratives, investment cases, operating model design, and performance metrics.
Operate with enterprise mindset; make transparent value, risk, and capacity trade-offs; build inclusive and high-performing teams; influence senior stakeholders through clear narratives and metrics; raise product management standards across teams; and balance strategic ambition with pragmatic delivery and operational excellence.