UAE National - Data Science Manager | Group Technology | Corporate Services
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 enable 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.
Overview of the Role:
As the Data Science Manager, you will play a critical role in designing, developing, and deploying next-generation AI systems across various domains. This includes working with large language models (LLMs), Co-pilot, agentic AI, and other advanced AI applications. You will leverage a variety of cutting-edge techniques, such as semantic learning, reinforcement learning, deep learning, natural language processing (NLP), computer vision, and multi-modal learning to build intelligent, scalable, and adaptive AI-driven solutions.
Your expertise will extend beyond conversational agents to areas like autonomous decision-making systems, personalized recommendation engines, predictive analytics, and advanced automation. As part of the team, you'll contribute to creating highly dynamic, context-aware, and adaptive AI systems that interact seamlessly with users, devices, and external platforms.
What You Will Do:
- Innovative AI Development: Design, develop, and deploy cutting-edge AI systems, including large language models (LLMs), Co-pilot, agentic AI, and other advanced AI-driven applications.
- Diverse AI Techniques: Utilize a wide range of AI methodologies such as semantic learning, reinforcement learning, deep learning, natural language processing (NLP), computer vision, and multi-modal learning.
- AI System Scalability & Performance: Build scalable, efficient, and adaptive AI models capable of handling diverse use cases across different lines of business and platforms.
- Beyond Chatbots: Work on applications beyond conversational agents, including autonomous decision-making systems, personalized recommendation engines, predictive analytics, and advanced automation.
- AI-Powered Interactions: Focus on creating dynamic, context-aware, and personalized AI systems that enhance user interaction with virtual assistants, autonomous agents, and AI-powered co-pilots.
- Continuous Research & Innovation: Stay at the forefront of AI research and trends, implementing the latest AI techniques and continuously optimizing models to improve performance and functionality.
- Collaborative Development: Collaborate with cross-functional teams to develop and deploy solutions that integrate AI into complex, real-world environments, ensuring high levels of user satisfaction and impact.
- End-to-End Solution Design: Take responsibilities for the end-to-end lifecycle of AI solutions, from research and development to deployment and maintenance.
Required Skills to Be Successful:
- UAE national
- 10+ years of experience in Data Science
About the Team:
You will be reporting to the Data Science Lead.
What Equips You for the Role:
- A Master's degree in Statistics, Data Science, Machine Learning, Mathematics, Computer Science, or a related quantitative field.
- 10+ years of experience in Data Science, with at least 4+ years focused on developing and deploying Gen AI-based solutions.
- Deep Learning & Neural Networks: Strong experience in deep learning frameworks like TensorFlow, PyTorch, Keras, and MXNet for building, training, and deploying neural networks.
- Natural Language Processing (NLP): Expertise in NLP techniques, including tokenization, named entity recognition (NER), sentiment analysis, topic modeling, and sequence-to-sequence models (e.g., transformers, BERT, GPT).
- Large Language Models (LLMs): Proficiency in working with LLMs such as GPT-3, GPT-4, T5, BERT, or custom transformer-based models for building conversational AI, co-pilots, and agentic systems.
- Vector Databases & Embedding Models: Hands-on experience with vectorizing data and working with vector databases (e.g., Pinecone, Faiss, Milvus, Weaviate) to enable similarity search, efficient retrieval, and semantic search across large datasets.
- Semantic Learning & Representation Learning: Knowledge of semantic learning techniques and representation learning methods for improving model understanding, reducing bias, and enhancing contextual accuracy.
- Multi-Modal AI: Experience in multi-modal learning, combining various data types (e.g., text, audio, video) to develop intelligent systems capable of handling complex, diverse inputs.
- Machine Learning Frameworks & Tools: Proficient in ML libraries like Scikit-learn, XGBoost, LightGBM, and other tools for model training, optimization, and evaluation.
- Cloud Computing & AI Infrastructure: Experience with cloud platforms (AWS, Google Cloud, Azure) and deploying AI models in cloud environments using Kubernetes, Docker, or serverless architectures.
- API Development & Integration: Familiarity with developing RESTful APIs for integrating AI models into production systems and working with API frameworks (Flask, FastAPI).
- Version Control & Collaboration Tools: Proficient with Git, GitHub, or GitLab for version control and collaborative development in a team-oriented environment.
- MLOps & Continuous Integration: Knowledge of MLOps practices for managing the AI lifecycle, including model versioning, CI/CD pipelines, and automation tools (e.g., Jenkins, MLflow).
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