Overview

Data Science & AI Lead

Responsibilities

• Own the AI and advanced analytics portfolio, including identification, sizing, prioritisation, and benefit tracking with business sponsors.
• Agree benefit measurement with Finance before work starts; report validated benefit rather than self-declared benefit.
• Review the portfolio regularly and scale, pause, or stop use cases based on evidence.
• Deliver Revenue Growth Management analytics, including price-pack architecture, price elasticity, price and trade promotion optimisation, and trade spend return.
• Build toward AI-supported price and mix optimisation, assortment, and channel analytics across business units and GCC markets.
• Partner closely with commercial leadership so models inform real pricing and promotion decisions.
• Deliver demand forecasting, inventory and replenishment optimisation, and route optimisation.
• Build cost-to-serve optimisation and distribution analytics to improve margin per customer and channel.
• Deliver manufacturing and quality use cases, including predictive maintenance, yield and waste reduction, quality prediction, and cold-chain analytics.
• Scale Generative AI across the enterprise, including assistants, document and contract intelligence, and function-level automation.
• Design and deploy AI agents that automate workflows and drive measurable efficiency across business and support functions.
• Establish MLOps, including experiment tracking, model registry, deployment pipelines, monitoring, and retraining.
• Define and enforce Responsible AI controls, including approved use-case registers, human oversight, bias and performance reviews, and model documentation.
• Lead a small team of Seniors, ICs, and Specialists, allocating work, reviewing quality, and developing capability.
• Direct offshore and vendor resources working within the assigned area.
• Set and enforce technical standards through review and practical implementation.
• Build trusted relationships with business unit and corporate function leaders and understand how each business makes money and decisions.
• Own initiatives end to end, from problem definition and business case development through delivery, adoption, and benefit realisation.
• Apply product management discipline with a defined backlog prioritised by value and a named business owner for every deliverable.
• Run delivery transparently with clear plans, visible progress, early escalation of risks and dependencies, and honest reporting of delays.
• Direct vendor and offshore resources and influence stakeholders to secure data ownership and adoption.
• Present confidently to senior leadership, translating technical topics into business language and clear recommendations.

Qualifications

• Every model should be tied to a decision that improves and a measurable business outcome, with a clearly identified beneficiary.
• Instrument solutions so benefits are measurable and build for reuse so future deployments cost less.
• Own the value case for the assigned area: sponsor, baseline, and target benefit agreed before work begins, with benefits reported after delivery.
• 8–12 years of experience in Data Science, Advanced Analytics, or AI Engineering.
• Proven experience taking models from prototype into production with measurable business benefits.
• Practical enterprise GenAI delivery experience, including RAG, assistants, agents, evaluation, and guardrails.
• Exposure to RGM, pricing, forecasting, supply chain, or manufacturing analytics within retail, FMCG, or a comparable sector.
• Demonstrated senior-level stakeholder management and end-to-end project or product management experience, from business case through adoption and benefit realisation.
• Experience directing vendors, partners, or offshore delivery resources.
• Bachelor's degree in Computer Science, Engineering, Information Systems, a quantitative discipline, or related field. Relevant cloud and data certifications are preferred.
• Experience within the GCC or a multi-entity, multi-country organisation is an advantage. Arabic language skills are beneficial but not mandatory.
• Python and ML frameworks such as scikit-learn, XGBoost or LightGBM, PyTorch or TensorFlow, along with strong SQL.
• Databricks, including Mosaic AI, MLflow, Feature Store, Model Serving, Vector Search, and Spark for scaled processing.
• Azure AI Foundry and Azure OpenAI, including LLM application patterns, agent frameworks, evaluation, and cost control.
• Forecasting, optimisation, elasticity, and experimentation methods.
• MLOps and software engineering fundamentals, including Git, testing, containers, APIs, and CI/CD.
• Power BI or Fabric integration to surface model outputs.
• Cloud data environments, with Azure strongly preferred; AWS or GCP experience considered.
• Experience working across ERP, sales/POS, supply chain, manufacturing, and HR source systems.
• Strong understanding of visualisation and BI tooling, with Power BI preferred; Tableau or Qlik considered.
• Advanced MS Excel and MS PowerPoint skills.
• Working knowledge of data governance, master data concepts, and associated tooling.
• Familiarity with delivering data, analytics, or AI as products that are versioned, documented, and continuously improved.
• Value-creation mindset, starting from the business outcome and working backwards to the solution.
• Strong team player who collaborates effectively across verticals and locations.
• Positive and proactive approach with the ability to create momentum in a developing function.
• Strong problem-solving and ownership mindset.
• Hands-on and agile, with the ability to build directly, work in short increments, and adapt to feedback.
• Comfortable working in a greenfield environment where standards and processes are still evolving.
• Confident communicator with senior stakeholders, equally comfortable leading technical reviews and business-value discussions.