Data Science & AI Lead

Data Science & AI Lead
Advanced Analytics & AI | Grade 8
Department : Group Data & AI
Vertical :Advanced Analytics & AI
Grade :8
Level :Lead (HR Grade: Manager)
Reports To :Senior Manager – Advanced Analytics & AI
Location :Hybrid – Dubai and Abu Dhabi, as required by the business and leadership.
Travel :This role may require travel across relevant markets and delivery locations.
Experience :8–12 years
Context
AI capabilities are already being applied across areas such as customer service automation, vision-based plant safety monitoring, and commercial AI through technology partnerships. This vertical will scale these capabilities and build an advanced analytics and AI portfolio that creates measurable value across revenue growth management, supply chain, manufacturing, and enterprise productivity.
Role Purpose
This is a senior hands-on expert role responsible for building solutions, reviewing technical work, and leading a small team while remaining deeply involved in delivery.
The role operates through short, iterative cycles, with an emphasis on delivering working outputs early.
Value Creation Accountability
Every role in this function is accountable for value created, not activity delivered. For this role, that means:
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.
Key Responsibilities
Use-Case Portfolio & Value
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.
Commercial & RGM Analytics
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.
Supply Chain, Manufacturing & Operations AI
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.
GenAI, Agents & Responsible AI
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.
Leading Others
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.
Stakeholder & Delivery Management
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 & Experience
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.
Technical Skills
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.
Key Competencies
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.
What Success Looks Like
Priority use cases moved into production with measurable benefits validated with Finance.
RGM analytics supporting live pricing and promotion decisions.
GenAI and agent-based automation delivering measurable efficiency within at least one business function.
MLOps operating effectively, with models deployable and retrainable without vendor dependency.

Work arrangement
No