Data Scientist Al Futtaim Private Company (LLC)

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. Required Skills To Be Successful: 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.