Manager - AI
United States Steel
The Lead Data Scientist is responsible for leading the organization's data science capability, establishing enterprise standards for AI and advanced analytics, and ensuring models are developed, evaluated, and deployed responsibly. This role combines technical leadership, governance oversight, talent development, and stakeholder engagement to drive the effective and ethical use of AI across the enterprise.The Lead Data Scientist serves as a trusted advisor to business and functional leaders, providing expert guidance on AI opportunities, risks, vendor solutions, and investment decisions while building a strong community of practice among data science professionals.
This role is ideal for a technically strong people leader who can balance innovation with governance, develop enterprise data science capabilities, and help leaders make informed decisions about AI investments and opportunities.
Key Responsibilities:
Data Science Governance & AI Standards
- Establish and maintain enterprise-wide standards, methodologies, and governance frameworks for AI, machine learning, and advanced analytics solutions.
- Define model evaluation criteria, validation processes, and production-readiness standards to ensure quality, reliability, fairness, explainability, and compliance.
- Lead reviews of AI and data science initiatives to ensure adherence to enterprise policies, risk management practices, and technical standards.
- Partner with Legal, Compliance, Risk, IT, and business stakeholders to address emerging AI governance requirements.
- Own and maintain an enterprise AI model inventory, ensuring visibility into all models in development and production across the organization
- Define and enforce model documentation standards (e.g., model cards) to ensure transparency, explainability, and compliance
- Establish standards for production model monitoring, including performance tracking, drift detection, and lifecycle management
- Oversee ongoing model health reviews and ensure models are maintained\\, retrained, or retired as needed
- Lead and develop a team of data scientists, fostering technical excellence, innovation, and continuous learning.
- Set priorities, allocate resources, and oversee execution of enterprise data science projects.
- Coach and mentor team members in advanced analytics, machine learning, experimentation, and model deployment practices.
- Support workforce planning, talent development, succession planning, and capability-building initiatives.
- Evaluate internally developed and third-party AI solutions to ensure alignment with business objectives, technical requirements, and governance standards.
- Provide independent technical assessment of vendor claims, solution capabilities, implementation approaches, and potential risks.
- Advise business leaders on build-versus-buy decisions, AI investment opportunities, and emerging technology trends.
- Serve as a subject matter expert in AI, machine learning, predictive analytics, and data science best practices.
- Ensure team performs technical due diligence and independent validation of third-party AI solutions, including evaluation of model performance, risks, and documentation completeness
- Lead and facilitate the enterprise Data Science Community of Practice across business units and functions.
- Promote knowledge sharing, collaboration, standardization, and reuse of analytical methods and solutions.
- Establish forums, workshops, and learning opportunities that strengthen enterprise AI and analytics capabilities.
- Drive adoption of common tools, methodologies, and governance practices.
- Educate leaders on AI capabilities, limitations, opportunities, and risks to support informed decision-making.
- Develop executive-level materials that translate complex technical concepts into practical business insights.
- Partner with senior leaders to identify high-value AI opportunities and assess potential business impact.
- Support governance committees, executive reviews, and strategic planning discussions related to AI and advanced analytics.
- Contributes to the development and execution of enterprise AI and data science strategy.
- Monitor industry trends, regulatory developments, and emerging technologies to inform organizational direction.
- Identify opportunities to improve data science effectiveness, model performance, governance processes, and business outcomes.
- Establish and track key performance indicators related to model quality, adoption, value realization, and operational excellence.
- Own the data science workstream for priority AI initiatives, including methodology, quality, and delivery accountability
Required:
- Bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Engineering, Operations Research, or a related field.
- Seven plus years of experience in data science, machine learning, advanced analytics, or AI-related roles.
- 3+ years of experience leading teams, projects, or technical professionals.
- Experience developing, validating, and deploying predictive models and machine learning solutions.
- Strong understanding of AI governance, model risk management, and responsible AI principles.
- Demonstrated ability to communicate complex technical concepts to business and executive audiences.
- Master's degree or Ph.D. in quantitative or technical discipline.
- Experience in manufacturing, industrial operations, engineering, supply chain, or related industries.
- Knowledge of AI regulatory frameworks, model governance, and emerging responsible AI standards.
- Experience evaluating third-party AI platforms and technology vendors.
- Experience leading enterprise-wide analytics or AI transformation initiatives.
- Work Environment/Physical Requirements:
- Primarily office environment.
- Constantly operates a computer.
- Must be able to remain stationary 50% of the time.
- Ability to travel 10% of the time.