Data Scientist Lead
UBS Group AG (company site, opens in a new tab)15+ yrsMumbaiOn-siteDirect career pageEst. 50–90 LPA
Posted on 22 Sept
Reach people at UBS Group AG
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at UBS Group AG you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead end-to-end development and deployment of autonomous AI agents and NLP models, ensuring scalable, production-ready systems.
- Design and implement Retrieval-Augmented Generation pipelines and LLM-powered applications over structured enterprise data for advanced querying and insights.
- Maintain and enhance evaluation tools to monitor performance, reliability, and consistency of large language models throughout their lifecycle.
Minimum Requirements
- 5-6 years of experience in NLP / AI Data Science as explicitly mentioned in the JD.
- Bachelor's degree or higher in Machine Learning, Computer Science, Computational Linguistics, Data Engineering, or related technical field.
- Proven experience with autonomous agent systems, multi-step reasoning, and LLM application development on structured/tabular data.
- Experience with RAG architectures, embedding generation, vector databases such as FAISS, Pinecone, Weaviate, or pgvector.
Ideal Candidate Profile
- Experienced in building and deploying autonomous agentic AI systems involving planning, tool use, and feedback loops, demonstrating advanced strategic AI skills.
- Hands-on ability to collaborate across data engineering and platform teams to ensure reliable AI data pipelines, indicating strong operational execution in enterprise settings.
- Comfortable communicating complex technical concepts to diverse audiences, showing capability in cross-functional stakeholder engagement.