Core Job Responsibilities Strategic Business Partnership and Cross-Functional Alignment Act as a trusted Data and Analytics leader, helping business functions identify opportunities to leverage data, analytics, AI, and GenAI for measurable outcomes.
Build strong relationships across Commercial, Marketing, R&D, Quality, Operations, Supply Chain, and Finance to understand priorities and challenges.
Translate business needs into actionable Data, Analytics, AI, and Data Product initiatives, and lead cross-functional discussions to drive alignment and decision-making.
Data Product Leadership and Design Thinking Establish and scale the Data Product delivery model across Abbott Diabetes Care.
Champion product-centric ways of working and support adoption of Data Product principles across business domains.
Partner with stakeholders to define, prioritize, and govern Data Products, driving discovery, roadmap development, and value realization through KPIs.
Promote reusable, governed Data Products and support Data Mesh principles through domain-based ownership.
Data Engineering and Platform Excellence Champion modern data engineering principles and best practices.
Build strong data foundations with clear standards for data quality, observability, metadata management, lineage, and security.
Partner with Architecture and Engineering teams to ensure enterprise data assets are scalable, reliable, and optimized for analytics and AI.
Champion effective use of Databricks and lakehouse technologies to support Data Products, Analytics, AI, and GenAI workloads.
Technical Leadership, Solution Design, and Architecture Alignment Provide technical leadership across Data, Analytics, AI, and Data Product initiatives from concept through delivery.
Lead solution design, translating business requirements into scalable capabilities. Partner with Enterprise Architecture and Corporate IT to shape solution strategy and technology direction.
Evaluate trade-offs between business value, scalability, and feasibility, ensuring solutions are secure, governed, and reusable.
AI and Generative AI Leadership Lead identification and assessment of AI and GenAI opportunities across ADC.
Partner with stakeholders to define value-driven use cases and success metrics.
Evaluate opportunities for strategic value, feasibility, and alignment with legal, privacy, and cybersecurity requirements.
Collaborate with Legal, Privacy, Cybersecurity, Data Science, Engineering, and Delivery teams to support implementation.
Promote responsible AI practices and monitor business impact.
Portfolio Prioritization and Demand Management Drive delivery of Data, Analytics, AI, and Data Product initiatives in partnership with Corporate IT, Architecture, and business stakeholders, ensuring commitments are met on time and within budget.
Lead intake, evaluation, and prioritization of initiatives.
Facilitate investment trade-off discussions and maintain visibility into progress, risks, and dependencies.
Provide portfolio recommendations to leadership. Business-Led Data Governance and Stewardship Champion a business-led governance model that promotes ownership and accountability for critical data assets.
Establish data stewardship and quality practices across business domains.
Promote metadata management, cataloging, and compliance best practices to strengthen trust and usability of enterprise data.
Advocate for trusted, governed, AI-ready data assets that support strategic objectives.
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