About US

Material is a pioneering global partner in strategy, insights, design, and technology, driving customer centricity and digital relevance in a rapidly evolving, customer-led marketplace. Leveraging cutting-edge, science-based tools, Material offers unparalleled human understanding to forge transformative relationships between businesses and their customers. Through bespoke customer-centric business models, immersive experiences, and robust measurement systems, we empower businesses to thrive amidst ongoing digital transformation

Lead Data Scientist

As the Lead Data Scientist, you will lead a team in developing AI solutions for metadata extraction, retrieval-augmented generation (RAG), and text-to-SQL models. Your role involves designing AI-driven solutions for schema retrieval, database integration, and model deployment in Azure.

Responsibilities:

• Research, design, and develop ML models for metadata extraction, information retrieval, and RAG applications.
• Define AI architecture, tools, and Azure integrations, optimizing cloud-based model deployment.
• Implement schema retrieval pipelines using embeddings, FAISS/VectorDB, and ranking mechanisms.
• Develop pipelines for data preprocessing, metadata structuring, and structured query generation.
• Apply MLOps principles for scalable, automated AI/ML workflows.
• Enhance bot interactions with natural language processing, improving accuracy and reducing hallucinations.
• Deploy AI models and APIs on Azure, optimizing performance and cost.
• Perform testing for functional, integration, and load scenarios, ensuring query accuracy.
• Collaborate with cross-functional teams to define requirements and drive AI solutions.

Must Have Skills:

• 6+ years in AI/ML, NLP, Python, with a strong focus on Azure.
• Expertise in RAG, NLP, Computer Vision, schema retrieval, and deep learning.
• Experience with FAISS/Vector DB, SQL, data preprocessing, and database integration in OneLake.
• Hands-on deployment and optimization of AI-powered bots and LLMs.
• Proficiency in microservices, Docker, and Kubernetes (Azure Kubernetes Service preferred).
• Strong MLOps practices, including CI/CD, monitoring, and lifecycle automation.
• Ability to optimize AI models for performance and efficiency in production.
• Strong Python skills with TensorFlow, PyTorch, LangChain, and FAISS.
• Excellent communication skills for technical and non-technical audiences.