About this role
COMPANY BACKGROUND United States Steel Corporation (U.S. Steel), founded in 1901, is a leading American steel producer headquartered in Pittsburgh, Pennsylvania. It was the world’s first billion-dollar corporation, formed through a merger involving J.P. Morgan, Andrew Carnegie, Charles Schwab, and Elbert H. Gary. The company has been pivotal in supplying steel for U.S. infrastructure, military needs, and economic growth. In 2025, U.S. Steel finalized a historic $14 billion partnership with Nippon Steel Corporation, retaining its name, U.S. headquarters, and “made in America” status. This deal enhances its capabilities through shared expertise in advanced steelmaking. The 2021 acquisition of Big River Steel marked its shift toward sustainable, low-emission mini-mill operations. U.S. Steel operates integrated mills (blast furnaces) and mini-mills (electric arc furnaces), producing 17–20 million tons of steel annually. It employs around 20,000–25,000 people and serves industries like automotive, construction, energy, and appliances. Specialties include Integrated Steel Production, Steel Process & Product Technology, Steel Development Research, Coke (Fuel) Production; Iron Ore Mining, Industries: Automotive, Oil & Gas, Appliance, Container, Industrial Machinery & Construction, Sustainable Steel, Electric Arc Furnace, green steel, and electrical steel. THE OPPORTUNITY & THE ROLE The Senior Manager of Enterprise Data & AI Enablement is responsible for leading execution and operationalization of AI-ready data capabilities across the enterprise in alignment with the enterprise Data and AI Strategy. This role ensures that enterprise data is discoverable, trusted, complete, timely, and fit-for-purpose to support both operational and strategic AI use cases across manufacturing, supply chain, commercial, and corporate domains. This leader plays a critical leadership role in shifting the organization from data availability to practical data readiness for AI, operating through influence within a federated, business-aligned data ecosystem. KEY RESPONSIBILITIES AI-Ready Data Foundations • Lead execution of AI-ready data standards and frameworks aligned to the enterprise data strategy to ensure data assets meet the quality, completeness, and consistency standards required for ML and advanced analytics. • Partner with AI, analytics, and business teams to align data preparation priorities with high-value AI use cases. • Establish clear criteria for “data readiness” to support AI/ML model training, inference, and monitoring (e.g. freshness/latency tiers). Metadata Management & Data Discoverability • Lead adoption of enterprise metadata management and data cataloging, ensuring critical data assets are discoverable and well described. • Enable assets include business/technical/operational metadata, data lineage, ownership, quality indicators, and appropriate usage guidance for AI consumption. Data Domains & Data Asset Register • Partner in the definition and lead the operationalization of enterprise data domains (e.g., manufacturing, supply chain, customer, finance), aligned to business capabilities. • Establish and maintain an authoritative enterprise data asset register, capturing critical datasets, owners, stewards, and usage patterns. • Drive accountability for data assets across federated domain teams (ownership and stewardship expectations). Data Governance, Quality & Standards • Define and implement data governance and data quality frameworks with measurable KPIs aligned to AI and business needs, including strategies related to data completeness, validation, deduplication, cleansing, standardization, and proofing to ensure datasets are suitable for AI and ML solutions. • Establish standards for data ownership, stewardship, and metadata management, ensuring accountability across enterprise data domains. • Establish data quality scorecards by domain, providing transparency into accuracy, timeliness, consistency, and completeness, and using these to drive prioritization and continuous improvement across federated teams. • Balance centralized governance standards with domain-level ownership and execution. Manufacturing & Operational Data Integration • Collaborate with engineering and data science teams to ensure operational data can be effectively leveraged • Enable integration of complex manufacturing data sources including MES, ERP, Level-2 plant systems, and high-volume time-series platforms (e.g., Aveva PI) • Ensure high-volume plant data can be reliably ingested, curated, and made available for analytics and AI use cases Data Architecture & Platform Collaboration • Partner with enterprise architecture and platform teams to operationalize a federated data architecture that enables domain ownership while enforcing enterprise standards for interoperability, quality, security, and performance. • Define latency and data freshness requirements aligned to different AI and analytics use cases (real-time, near-real-time, batch). • Provide oversight and alignment for data integration and sharing patterns (ETL/ELT, orchestration) while partnering with platform teams for implementation. • Partner with cloud, platform, security, and integration teams to ensure architectures effectively support AI workloads. Leadership Expectations • Serve as a strategic leader for enterprise data and AI enablement, leading through influence and credibility in a federated environment, • Develop capabilities and ways of working across governance and enablement; drive shared ownership of the data quality and AI readiness. • Drive adoption of enterprise data standards through change management, communication, and stakeholder engagement across plants and business domains. • Promote a culture of accountability for data quality, AI outcomes, and responsible data stewardship. REQUIRE