Vice President - Data Scientist Lead Role
JPMorgan Chase & Co. (company site, opens in a new tab)5–7 yrsBengaluruOn-siteDirect career pageEst. 35–55 LPA
Posted on 3 Sept
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Job description
Structured overview of role & requirements
About This Role
- Develop and deploy GenAI/LLM solutions covering content extraction, semantic search, QA, summarization, reasoning, and recommendation with measurable performance frameworks.
- Design and operate prompt-based and RAG-based systems, including orchestration and multi-step reasoning workflows; collaborate with engineering for scalable, production-grade API and pipeline delivery.
- Lead data pipeline design for structured/unstructured data, perform applied research translating state-of-the-art NLP methods into improvements, mentor junior scientists, and manage AI governance and responsible AI practices.
Minimum Requirements
- Advanced degree (Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative fields, or equivalent practical experience.
- 5-7 years of applied experience building ML/NLP solutions with production deployment in fast-paced environments.
- Proven expertise in NLP and LLM technologies including prompt engineering, RAG, evaluation methods; hands-on experience with Amazon Bedrock or equivalent managed LLM platforms and Cortex analytics workflows.
- Strong Python programming skills; familiarity with ML/DL frameworks (PyTorch or TensorFlow); experience in data pipelines (including embeddings, vector search); software engineering best practices (version control, testing, CI/CD).
Ideal Candidate Profile
- Experienced in delivering scalable GenAI solutions in enterprise or financial services environments with deep LLM and prompt engineering expertise.
- Skilled in integrating deployed ML/NLP models into production APIs and services, with demonstrated applied research translating academic methods to practice.
- Capable of operating across technical leadership, stakeholder communication, and mentoring, including governance of responsible AI and safety evaluation methodologies.