Data Scientist jobs in United Arab Emirates

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Established in the 1930s as a trading business, Al-Futtaim Group today is one of the most diversified and progressive, privately held regional businesses headquartered in Dubai, United Arab Emirates. Structured into five operating divisions; automotive, financial services, real estate, retail and healthcare; employing more than 35,000 employees across more than 20 countries in the Middle East, Asia and Africa, Al-Futtaim Group partners with over 200 of the world’s most admired and innovative brands. Al-Futtaim Group’s entrepreneurship and relentless customer focus enables the organization to continue to grow and expand; responding to the changing needs of our customers within the societies in which we operate.

By upholding our values of respect, excellence, collaboration and integrity; Al-Futtaim Group continues to enrich the lives and aspirations of our customers each and every day

• The Data Scientist role at Al-Futtaim is centered on designing, building, and productionizing advanced analytics and GenAI-powered solutions. The purpose of the role is to enhance insights, recommendations, and decision-making across enterprise platforms. Success is measured by the ability to develop scalable, reliable machine learning and GenAI models that meet enterprise standards of governance, security, and compliance. The incumbent will collaborate with cross-functional teams to deliver impactful data-driven insights.

Advance Analytics & Data Science

• Translate business problems into data science, statistical, and machine learning solutions that drive measurable outcomes across enterprise use cases.
• Perform data exploration, feature engineering, model development, and evaluation on large-scale structured and semi-structured datasets.
• Build and deploy predictive, prescriptive, and descriptive models, ensuring interpretability, robustness, and alignment with business objectives.
• Partner closely with business, product, and analytics teams to validate assumptions, define success metrics, and deliver actionable insights.

Applied GenAI & LLM Enablement

• Apply GenAI techniques to augment data science workflows, including LLM-based insight generation, summarization, classification, and decision support.
• Design and implement Retrieval-Augmented Generation (RAG) solutions to ground LLM outputs in enterprise data and analytical results.
• Collaborate on GenAI-enabled analytical applications with a focus on accuracy, relevance, and explainability.
• Evaluate and benchmark GenAI outputs using quantitative and qualitative metrics, ensuring alignment with business and analytical standards.

Enterprise Productionization & MLOps/ LLMOps

• Productionize data science and GenAI models using enterprise‑grade MLOps / LLMOps practices, including versioning, deployment, monitoring, and retraining strategies.
• Build scalable, secure, and reliable analytical pipelines in collaboration with Data Engineering and Cloud teams.
• Monitor model performance, data drift, and GenAI output quality, and drive continuous improvements based on real‑world usage.
• Ensure solutions meet enterprise requirements for governance, security, compliance, and responsible AI.

Performance Measurement & Continuous Improvement

• Define and track model and GenAI performance metrics (accuracy, stability, bias, latency, business impact).
• Run experiments and controlled rollouts to optimize models, GenAI prompts, and retrieval strategies.
• Continuously enhance solutions through feedback loops, experimentation, and evolving business needs.

• Strong foundation in statistics, machine learning, and applied data science.
• Proficiency in Python, SQL, and Spark with expertise in data processing and analytical pipelines.
• Hands‑on experience with the Databricks ecosystem for deploying data science and GenAI solutions.
• Practical exposure to MLOps / LLMOps practices, including model versioning and monitoring.

This role reports to Data Science Lead

• 8+ years of experience in Data Science / AI Engineering, focusing on building and deploying machine learning models, including supervised, unsupervised, and time‑series models.
• 6+ years of experience in feature engineering, model evaluation, and performance optimization.
• 4+ years of experience with NLP or language‑based systems, including text classification, information extraction, and semantic modeling.
• 2+ years of experience in delivering GenAI or conversational AI solutions in production, focusing on applied LLM use cases, RAG, and enterprise deployment.