VP of AI, Data and Data Science

Sims Limited•Chicago, IL

About The Position

The Vice President, AI, Data and Data Science leads Sims Limited’s global AI, data, analytics and data-science capability. Reporting to the Chief Technology Officer and serving on the Technology Leadership Team, this executive defines strategy, builds teams and partner ecosystems, establishes architecture and controls, and delivers solutions intended to improve safety, throughput, yield, quality, reliability, customer outcomes, working capital, cost and employee productivity. The role brings operational technology and industrial data-science experience to connect plant, yard, logistics, asset, commercial and enterprise data with advanced analytics, machine learning, generative AI, computer vision, optimization and intelligent automation. The leader works directly with operational and business leaders to identify high-value problems, demonstrate value, industrialize successful solutions and embed new ways of working. This is a builder role responsible for establishing an enterprise AI and agent delivery model, with reusable patterns and secure development environments for evaluation, deployment, monitoring and lifecycle management. The portfolio includes Microsoft AI capabilities, custom-built agents and applications, and approved advanced engineering tools. Accountability covers business outcomes, adoption, responsible use, architecture quality and sustainable support. The position manages and expands the existing enterprise data, certified reporting and business-intelligence capability while building broader AI, data-science, data-engineering and agent-engineering teams. It influences AI and data architecture, data quality and master-data direction, combining internal talent, strategic partners and specialist managed services while retaining internal ownership of strategy, architecture, critical intellectual property and business outcomes.

Requirements

Significant executive or senior leadership experience across AI, data, analytics, data science and technology delivery in a global or complex organization.
Demonstrated success applying data science and AI in industrial, operational technology, manufacturing, mining, recycling, logistics, energy, supply-chain or other asset-intensive environments.
Experience taking AI, machine-learning or optimization solutions from business problem through production deployment, adoption, monitoring and value realization.
Hands-on understanding of modern AI architecture, generative AI, agents, retrieval, model evaluation, MLOps/LLMOps, data engineering, cloud platforms, APIs and secure software delivery.
Experience building or materially scaling multidisciplinary teams spanning BI, enterprise data, data engineering, data science, AI/ML engineering, agent engineering and product delivery.
Experience establishing enterprise data platforms, certified reporting, self-service analytics, governance, data quality, master data, metadata and security capabilities.
Ability to engage credibly with operators and engineers at sites as well as the CEO, ELT and Board.
Strong commercial judgment across build-buy-partner decisions, strategic vendors, managed services and technology investment.
Broad CTO-level experience across multiple technology domains, with a history of developing leaders, building succession depth and leading significant organizational change.
Bachelor’s degree in computer science, data science, engineering, mathematics, operations research or a related discipline, or equivalent professional experience.

Responsibilities

Partner with the CTO, Executive Leadership Team and business leaders to define and execute a global AI, data and analytics strategy aligned with enterprise, Metals and SLS priorities.
Create a value-led roadmap connecting operational, commercial and functional opportunities to business owners, investments, milestones, adoption plans and measurable benefits.
Move the organization from experimentation to governed, repeatable delivery, making clear decisions on what to build, buy, partner on, scale, stop or retire.
Provide decision-ready updates to the CTO, ELT, Board and governance forums on value, delivery, risk, adoption and capability maturity.
Build a portfolio of operational AI and data-science solutions across yards, plants, logistics, maintenance, asset recovery and processing operations.
Apply analytics, forecasting, optimization, machine learning, computer vision and generative AI to business problems including safety, throughput, yield, quality, downtime, energy use, inventory, routing, pricing and material recovery.
Integrate OT, IoT, equipment, telemetry, edge and enterprise data safely and reliably with operations, engineering, infrastructure and cybersecurity leaders.
Establish processes for site discovery, proof of value, controlled pilots, industrialization, adoption and global replication of successful use cases.
Monitor models in operational conditions, including retraining, exception management and accountable human oversight.
Establish the capability to design, develop, test, deploy and operate AI agents, copilots and custom AI-enabled applications.
Define reference patterns for agent orchestration, retrieval, tool use, model selection, identity, permissions, memory, observability, evaluation, guardrails and human approval.
Build and manage a repository of reusable agents, prompts, skills, components, APIs and evaluation assets with clear ownership and lifecycle controls.
Enable disciplined use of Copilot, Copilot Studio, Azure AI services and approved custom-development environments such as Claude Code.
Create secure development-to-production pathways that include source control, automated testing, red teaming, release management, monitoring, support and retirement.
Partner with application, infrastructure, cybersecurity, Enterprise Architecture and OT teams to integrate AI solutions into business processes and support them as production products.
Shape and influence the target architecture and roadmap for enterprise data, analytics, AI and data science in partnership with Enterprise Architecture.
Own enterprise data and certified reporting, and build trusted, reusable data products and governed semantic layers for executive reporting, operational applications, AI and self-service analytics.
Lead data engineering, integration, master-data management, metadata, lineage, quality, retention and access-control capabilities.
Lead practical data governance, data quality and master-data management with named business owners, clear definitions, measurable quality and timely resolution of critical data issues.
Develop an enterprise data-science platform and model/agent operations capability that supports experimentation within production controls.
Reduce duplication and technical debt across analytics tools, data stores, models, reports and integrations.
Create a strategic co-innovation agenda with priority partners focused on business outcomes.
Shape pilots, reference architectures and adoption programs across Claude, Microsoft 365 Copilot, Copilot Studio, Azure, Fabric, Power Platform and related AI capabilities where appropriate.
Challenge partners to contribute industrial expertise, engineering capacity, reusable assets, commercial flexibility and measurable value.
Build a selective ecosystem of data-science, OT, cloud, model, engineering and change partners while avoiding fragmented ownership and vendor dependency.
Protect Sims’ data, intellectual property, architecture choices and negotiating leverage in partner arrangements.
Establish enterprise AI governance that enables speed while managing security, privacy, legal, regulatory, ethical, safety and operational risks.
Define use-case intake, risk tiering, design review, model and agent evaluation, approval, monitoring, incident response and retirement processes.
Lead adoption through role-based learning, AI ambassadors, communities of practice, communications, playbooks and embedded change support.
Measure realized value through agreed operational, financial, risk, customer and workforce outcomes rather than activity or license consumption alone.
Address workforce implications transparently, including job redesign, skills development, capacity shifts and responsible human oversight.
Lead the global data, analytics and business-intelligence team and build AI, data-science, data-engineering, agent-engineering and architecture capabilities.
Define the organization model, role accountabilities, career paths, critical-skill plans and succession coverage.
Recruit, develop and retain leaders and practitioners with technical depth, business acumen and delivery discipline.
Create multidisciplinary product teams with operations, OT, process, application, cybersecurity, infrastructure and change expertise; partner with existing OT data and AI engineering resources.
Use partners and managed services for scale and specialist capacity while retaining Sims product ownership, architecture authority and critical knowledge.
Build a culture of first principles, curiosity, responsible experimentation, reuse, documentation, measurable delivery and continuous improvement.
Contribute to broader technology strategy, investment choices, operating-model decisions, cybersecurity and resilience priorities, architecture, talent and major transformation programs.
Represent the CTO in agreed executive, business, partner and governance forums and lead selected cross-functional priorities.
Contribute beyond the AI and data portfolio as a leader across the full Technology function.
Build trusted relationships with the CEO, ELT, Board, regional leaders and operational executives through clear communication, commercial understanding and reliable follow-through.