Director, Data & AI

Responsibilities

• Develop and execute the YAA wide data strategy and AI roadmap in alignment with DVCS and market priorities.
• Define strategic focus areas for data modernization, analytics, and responsible AI adoption to support YAA's business outcomes.
• Lead the development of federated data governance models and dataasaproduct operating principles across functions and markets.
• Establish and maintain enterprise data governance frameworks including data ownership, stewardship, policies, standards and procedures.
• Ensure data quality, security, privacy, cataloging, lineage, metadata management, and access control are consistently implemented across DVCS/YAA.
• Champion compliance with internal guidelines and regulatory requirements (AI safety, privacy regulations, model risk controls).
• Oversee the design and evolution of data platforms including cloud data lakes, warehouses, integration pipelines, semantic models and analytics tooling.
• Ensure interoperability, scalability, reusability, and cost-efficient architecture to support both operational and advanced analytics use cases.
• Guide prioritization and delivery of reusable data products, shared datasets, APIs, and platform capabilities.
• Lead end-to-end lifecycle of AI/ML development: opportunity identification, model design, experimentation, deployment and monitoring.
• Ensure all AI solutions adhere to responsible AI standards, including human-in-the-loop, model explainability, bias mitigation and operational controls.
• Build consistent model Machine Learning Operations practices for versioning, retraining, governance, and Drive delivery of advanced analytics, predictive and prescriptive models, and data driven insights to support YAA's commercial, operational, agronomic and financial use cases.
• Ensure high quality insight products (dashboards, KPIs, storytelling visualizations) that enable decision making at scale.
• Embed value tracking frameworks to measure impact on OKRs, P&L, operational efficiency and customer outcomes.
• Lead, mentor and develop data engineers, data analysts, AI/ML engineers and related roles to build strong capabilities within YAA/DVCS.
• Foster a culture of innovation, continuous learning, experimentation, and cross functional collaboration.
• Define career pathways, upskilling programs and communities of practice for data and AI across the region.
• Partner with market teams, product teams, technology, commercial, agronomy, supply chain and corporate functions to solve business problems using data and AI.
• Translate complex business challenges into actionable data & AI solutions with clear business value.
• Influence senior stakeholders across YAA, markets and global teams to drive shared priorities and alignment.
• Lead structured change management initiatives to drive adoption of data platforms, analytics tools and AI solutions across YAA.
• Elevate data literacy and responsible AI awareness through training, communication, and adoption programs.
• Embed adoption KPIs into rollout processes to ensure measurable uptake across markets/functions.
• Work closely with Legal, Data Protection, IT Security and Compliance to ensure safe and compliant deployments.

Profile

Director, Data & AI is accountable for defining and executing the enterprise data strategy and AI roadmap to accelerate business outcomes and build a truly datadriven organization.

Partnering across markets and functions, this role establishes a federated data and AI operating model, anchored in strong governance, dataasaproduct principles, and a pragmatic Center of Excellence in YAA to scale repeatable capabilities.

The Director oversees endtoend data management (architecture, quality, stewardship, cataloging, and access), leads the design and delivery of highvalue analytics and AI solutions from discovery through production, and drives adoption through change management, education, and measurable business value realization.

The role ensures AI safety, compliance, and model risk controls are embedded by design, and steers investment toward platforms and reusable data products that shorten timetoinsight, improve decision quality, and unlock new sources of growth and efficiency. Success is evidenced by broad stakeholder adoption, uplift in trusted data availability and quality, responsible AI usage at scale, and tangible P&L/OKR impact aligned to Country/Market, DVCS and YAA strategic objectives.

Additional Information

1. Education & Foundational Background

• Bachelor's degree in Data Science, Engineering, Computer Science, Business Analytics, Information Systems or an equivalent technical field.
• 6-10 years of hands-on experience in data analytics, data engineering, data management, business intelligence or AI/ML solution delivery.
• Prior experience leading small teams, projects, or data workstreams with readiness to grow into broader leadership responsibilities.

2. Technical Knowledge

• Solid understanding of data fundamentals - data modelling, metadata, data quality, data lifecycle, and basic data governance concepts.
• Experience working with modern data platforms such as cloud data lakes/warehouses, ETL/ELT pipelines, dashboards and reporting tools.
• Familiarity with AI/ML concepts (model training, evaluation, monitoring, and deployment)
• Ability to collaborate with data engineers, analysts and AI/ML practitioners to translate business needs into technical requirements.

3. Governance, Privacy & Compliance Awareness

• Basic understanding of data privacy regulations, internal data handling expectations, and responsible AI principles.
• Ability to follow established governance processes, escalate risks, and support compliance with security and privacy guidelines.
• Awareness of risk factors in AI solutions (data sensitivity, misuse, bias)

4. Leadership & People Skills

• Demonstrated ability to guide, mentor and support data analysts, engineers, or junior data specialists.
• Capable of prioritizing team workloads, setting expectations, and enabling cross functional collaboration.
• Strong communication skills with the ability to simplify technical topics for nontechnical audiences.
• Learning oriented mindset open to coaching and developing managerial maturity.

5. Stakeholder & Vendor Management

• Experienced working with internal business partners to understand needs, communicate insights, and support adoption.
• Exposure to evaluating vendors, tools or platforms
• Ability to support contract inputs, technical assessments or tool evaluations.

Contact details

Apply no later than

.