VP of AI, Data and Data Science

Sims Metal (Sims) is a global leader in metal recycling. With facilities across the globe, Sims plays an intrinsic role in the circular economy by making resources available for future use. As a responsible corporate citizen, we continuously seek new ways to broaden our participation in the environmental sector, ensuring that our future is as bright, safe, and secure as at any time in our long history.

Job Description

This is the enterprise leader accountable for turning AI and data into measurable operational and commercial advantage across Sims. The role combines strategy, hands-on delivery, data science, agent engineering, data platforms, architecture, governance, adoption, and talent leadership. It is not a reporting-only or innovation-lab role.

The successful candidate must build production capability, scale practical solutions globally and contribute as a broad enterprise technology leader across the Technology function.

Position Summary

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, the role is accountable for defining the strategy, building the team and partner ecosystem, establishing the architecture and controls, and delivering solutions that improve safety, throughput, yield, quality, reliability, customer outcomes, working capital, cost and employee productivity.

The role will bring deep operational technology and industrial data-science experience. It will connect plant, yard, logistics, asset, commercial and enterprise data with advanced analytics, machine learning, generative AI, computer vision, optimization and intelligent automation.

The leader will work directly with operations and business leaders to identify high-value problems, prove value quickly, industrialize successful solutions, and embed new ways of working.

This is a builder role. The leader will establish an enterprise AI and agent delivery model, including reusable patterns, secure development environments, evaluation, deployment, monitoring, and lifecycle management. The portfolio will include Microsoft AI capabilities, custom-built agents and applications, and fit-for-purpose use of advanced engineering tools such as Claude Code, where approved.

Accountability includes outcomes, adoption, responsible use, architecture quality and sustainable support, not simply pilots or technology demonstrations.

The position will manage and expand the existing enterprise data, certified reporting and business-intelligence capability while building a broader AI, data-science, data-engineering and agent-engineering team. This leader will materially influence AI and data architecture, data quality and master-data direction.

It will combine internal talent, strategic partners and specialist managed services, while retaining clear internal ownership of strategy, architecture, critical intellectual property and business outcomes.

Key Responsibilities

1. Enterprise AI and Data Strategy

• Partner with the CTO, Executive Leadership Team and business leaders to define and execute a global AI, data and analytics strategy aligned with Sims’ enterprise, Metals and SLS priorities.
• Create a value-led roadmap that connects operational, commercial and functional opportunities to clear business owners, investment requirements, delivery milestones, adoption plans and measurable benefits.
• Move the organization from experimentation to governed, repeatable delivery, with transparent decisions on what to build, buy, partner, scale, stop or retire.
• Provide clear, decision-ready updates to the CTO, ELT, Board and governance forums on value, delivery, risk, adoption and capability maturity.

2. Operational Technology, Data Science and Industrial AI

• Build a portfolio of operational AI and data-science solutions across yards, plants, logistics, maintenance, asset recovery and processing operations.
• Apply advanced analytics, forecasting, optimization, machine learning, computer vision and generative AI to practical business problems such as 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, in partnership with operations, engineering, infrastructure and cybersecurity leaders.
• Establish methods for site discovery, rapid proof of value, controlled pilots, industrialization, adoption and global replication of successful use cases.
• Ensure models remain effective in real operating conditions through monitoring, retraining, exception management and accountable human oversight.

3. AI Agents, Custom Solutions and Engineering

• Establish the enterprise 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 an enterprise repository of reusable agents, prompts, skills, components, APIs and evaluation assets with clear ownership and lifecycle controls.
• Enable disciplined use of modern AI engineering tools, including Copilot, Copilot Studio, Azure AI services and approved custom-development environments such as Claude Code.
• Create secure development-to-production pathways, including source control, automated testing, red teaming, release management, monitoring, support and retirement.
• Partner with application, infrastructure, cyber, Enterprise Architecture and OT teams so AI solutions are integrated into business processes and supported as production products.

4. Data Platform, Governance and Architecture

• Shape and influence the target architecture and roadmap for enterprise data, analytics, AI and data science in close partnership with Enterprise Architecture, which remains outside this role.
• Own enterprise data and certified reporting, and build trusted, reusable data products and governed semantic layers that support executive reporting, operational applications, AI and scalable 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 rapid resolution of critical data issues.
• Develop an enterprise data-science platform and model/agent operations capability that supports experimentation without bypassing production controls.
• Reduce duplication and technical debt across analytics tools, data stores, models, reports and integrations.

5. Strategic Partner Innovation

• Create a strategic co-innovation agenda with priority partners, focused on business outcomes rather than product deployment alone.
• Shape pilots, reference architectures and adoption programs across Claude, Microsoft 365 Copilot, Copilot Studio, Azure, Fabric, Power Platform and related AI capabilities where they fit Sims’ needs.
• Challenge partners to bring 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 or vendor dependency.
• Protect Sims’ data, intellectual property, architecture choices and negotiating leverage in all partner arrangements.

6. Adoption, Responsible AI and Value Realization

• 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, not activity or license consumption alone.
• Ensure workforce implications are addressed transparently, including job redesign, skills development, capacity shifts and responsible human oversight.

7. Organization, Talent and Delivery Leadership

• Lead the global data, analytics and business-intelligence team and build the future AI, data-science, data-engineering, agent-engineering and architecture capability.
• Define a clear organization model, role accountabilities, career paths, critical-skill plans and succession coverage.
• Recruit, develop and retain high-caliber leaders and practitioners who combine technical depth, business acumen and delivery discipline.
• Create multidisciplinary product teams with operations, OT, process, application, cyber, infrastructure and change expertise. Partner with current OT data and AI engineering resources, with the operating model able to evolve as capability matures.
• Use partners and managed services for scale and specialist capacity while retaining strong Sims product ownership, architecture authority and critical knowledge.
• Build a culture of first principles, curiosity, responsible experimentation, reuse, documentation, measurable delivery and continuous improvement.

8. Broader Enterprise Technology Leadership

• Operate as an enterprise technology executive, not solely a functional data and AI leader.
• Contribute to technology strategy, investment choices, operating-model decisions, cyber 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.
• Bring broad CTO-level experience and 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.

Key Accountabilities

• A business-aligned enterprise AI, data and analytics strategy with an executable, value-based roadmap.
• Production AI and data-science solutions that improve operational, commercial or workforce outcomes.
• A scalable, secure and governed agent-development and custom-AI engineering capability.
• Trusted data products, common definitions and practical data governance across priority domains.
• A coherent data, AI and analytics architecture with reduced duplication and technical debt.
• Measurable adoption and benefits realization, with transparent ownership and controls.
• An effective internal team and partner ecosystem with clear accountability and sustainable support.
• Credible executive and Board communication on opportunity, performance, investment and risk.
• Stronger leadership depth and succession coverage across the AI, data and Technology organization.

Key Relationships

• Chief Technology Officer
• CEO, ELT and Board stakeholders
• Technology Leadership Team
• Metals, SLS and regional business leaders
• Operations, Engineering and OT leaders
• Enterprise Architecture, Cybersecurity and Risk
• Finance, Legal, HR and Procurement
• Microsoft and strategic technology partners
• Data owners and process owners
• Universities, research and specialist AI partners
• Experience and Qualifications

Required

• 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.

Preferred

• Experience with metals, recycling, circular economy, material processing, heavy industry or data-center asset recovery.
• Experience with Claude Code, Microsoft Azure, Microsoft Fabric, Microsoft 365 Copilot, Copilot Studio, Power Platform and enterprise-scale Microsoft partnerships.
• Experience with edge AI, computer vision, IoT/telemetry, predictive maintenance, process optimization, or industrial digital twins.
• Experience with SAP, Dynamics 365 and integration of enterprise and operational data.
• Experience building custom AI solutions using contemporary coding assistants and model ecosystems, including approved use of Claude Code or equivalent tools.
• Master’s degree, MBA or advanced technical qualification relevant to AI, data science, engineering or business leadership.

Leadership Capabilities

• Enterprise judgment: Connects operational, commercial, workforce and technology choices to Sims’ strategy and risk appetite.
• Technical depth with pragmatism: Can challenge architecture and engineering decisions while staying focused on usable outcomes.
• Operational credibility: Builds trust with site and operations leaders by understanding real constraints and delivering in the field.
• Builder mindset: Creates teams, platforms, patterns, and products, rather than depending on strategy decks or vendors.
• Value orientation: Translates use cases into measurable outcomes and stops work that does not justify continued investment.
• Responsible innovation: Moves quickly while protecting safety, security, privacy, data and reputation.
• Talent leadership: Attracts outstanding people, develops successors and creates a high-accountability culture.
• Executive presence: Communicates complex choices clearly and earns confidence with senior executives and the Board.
• Partner leverage: Gets more value, innovation and accountability from strategic technology partners.
• Technology leadership breadth: Contributes credibly across enterprise applications, infrastructure, cyber, architecture, service delivery, transformation, investment and talent.

Measures of Success

• An enterprise AI and data roadmap is approved, funded, actively governed and visibly connected to business priorities.
• Priority AI, data-science and agent solutions progress from pilot to production and demonstrate agreed operational, financial, risk or productivity outcomes.
• Sims has repeatable, secure and supportable patterns for developing and operating agents and custom AI solutions.
• Priority operational and enterprise data domains have accountable owners, trusted datasets, common definitions, and improving data quality and master data.
• Business adoption expands through practical training, self-service analytics advocacy, embedded change and reusable solutions, not one-off demonstrations.
• The data and AI organization has clear roles, stronger technical depth, viable career paths and succession coverage.
• Microsoft and other strategic partners deliver measurable innovation, engineering capacity and commercial value against an agreed agenda.
• Executive and Board reporting clearly distinguishes facts, benefits, risks, decisions and required actions.
• The VP plays a visible leadership role across the broader Technology agenda and can represent the CTO in agreed forums.

Sims is proud to be an equal opportunity employer. We value the diversity of all of our employees and are committed to creating an inclusive working environment where everyone can contribute, advance on merit, and realize their full potential.

Sims does not discriminate based on race, sex, religion, color, national origin, citizenship status, disability, age, marital or familial status, sexual orientation, gender identity, gender expression, veteran status, housing status, source of income, or any other status protected by federal, state, or local laws.

This applies to any employment decision, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. Qualified applicants with a disability in need of a reasonable accommodation may request such without fear of reprisal or discrimination.

To find out more, please visit www.simsltd.com for more information on Sims and its commitment to sustainability.

Pay: $272,000.00 - $350,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Flexible spending account
• Health insurance
• Health savings account
• Life insurance
• Paid time off
• Parental leave
• Tuition reimbursement
• Vision insurance

Work Location: Remote