Senior Manager - AI Development

3368401No5238549710.00.031-May-2026Senior Manager - AI DevelopmentCOMPANY 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.

REQUIRED PROFESSIONAL EXPERIENCE & QUALIFICATIONS

The ideal candidate will bring a blend of enterprise data strategy, architecture, and governance experience with the ability to influence stakeholders across a large, federated organization.

Key experience includes:

• Experience defining or guiding enterprise data architecture and data integration strategies within complex enterprise environments
• Experience establishing data governance frameworks, metadata management, and data cataloging capabilities
• Familiarity with modern data platforms and Lakehouse architectures (e.g., Databricks, Snowflake, or similar cloud-based data environments)
• Understanding of data engineering concepts and data pipeline architectures (ETL/ELT), with the ability to guide and partner with engineering teams
• Experience working with manufacturing, industrial, or process-industry data environments, including operational or plant data sources such as MES, ERP, or high-volume time-series data
• Ability to operate effectively in a federated environment, influencing stakeholders across IT, engineering, and business teams
• Strong communication and leadership skills with the ability to translate technical data concepts into business value.14021BRUnited States Steel CorporationSenior Manager of Enterprise Data and AI EnablementPittsburgh

Since 1901, U. S. Steel has been a recognized leader in steel production. Today, as the first North American steel company to have declared a 2050 net-zero greenhouse gas emissions goal, we remain as innovative as ever, leading transformation across our industry while continuing to make products for everyday life - from industries as far ranging as automotive, construction, containers and packaging, appliances, and energy.

Underneath it all is our Culture of Caring, which shows up in our community partnerships, charitable contributions, company-sponsored employee volunteer initiatives, scholarship programs, leadership training, and much more. And of course, it takes shape in a steadfast commitment to safety first in our workplaces and respect for our employees, who are United by Steel.

We are honored to have earned accolades and awards from well-regarded organizations, including the following:

• Ethisphere's World's Most Ethical Companies 2022, '23, '24
• Disability: IN's Best Places to Work for Disability Inclusion 2021, '22, '23, '24
• Human Rights Campaign Foundation's Equality 100 Award 2020, '21, '22, '23-24, '25
• Military Times' Best for Vets: Employers 2023, '24

Conducting business with integrity and with the highest ethical values has underpinned U. S. Steel's success for over 100 years, and it remains critical to our company's success in the future. U. S. Steel is an Equal Opportunity Employer. It is our policy to provide equal employment opportunity (EEO) according to job qualifications without discrimination on the basis of race, color, religion, ancestry, national origin, age, genetics, sexual orientation, sex, gender identity, disability status or status as a protected Veteran or any other legally protected group status. (California residents may visit www.ussteel.com/CANotice regarding collection of personal information and U. S. Steel''s privacy practices.)

At U. S. Steel all employees are expected to display the following core competencies every day to advance corporate, team and individual goals:

Think: Think Critically and Drive Change

Lead: Develop Talent and Collaborate

Do: Empower Performance and Deliver Results