Data Scientist Senior Associate

JPMorganChase · Hyderabad · 8+ yrs experience · Posted 2026-07-25

Tech stack: AWS, Azure, GCP

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About the role

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Data Scientist Senior Associate within our Business Banking Data and Analytics Team, you will be instrumental in constructing predictive models and developing robust RAG pipelines. Collaborating closely with cross-functional teams, you will extract valuable insights from complex datasets to promote data-driven decision-making across the organization. Your focus will include addressing problem statements and innovating solutions for complex challenges. Additionally, you will build AI-based solutions to enhance both technological and business efficiency.
Responsibilities:
- Align ML problem definition with business objectives to ensure solutions address real-world needs.
- Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.
- Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.
- Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.
- Promote software and model quality, integrity, and security throughout the organization.
- Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.
- Integrate GenAI solutions with enterprise platforms using API-based methods.
- Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.
- Collaborate with technology teams to lead the design and delivery of GenAI products.
Qualifications:
- Formal training or certification in software engineering concepts and 8+ years of applied AI/ML experience.
- Strong understanding of the Software Development Life Cycle (SDLC), Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Proficiency in RDBMS, NoSQL databases, and prompt design.
- Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, pyG, Keras, MXNet, and Scikit-Learn.
- Proficient in building AI Agents (e.g., LangChain, LangGraph, AutoGen), integration of tools (e.g., API), and RAG-based solutions (e.g., open search), Knowledge Graphs(e.g., neo4J).
- Proven track record of building and scaling software and/or machine learning platforms in high-growth or enterprise environments.
- Exceptional ability to communicate complex technical concepts to both technical and non-technical audiences.

Qualifications

- Formal training or certification in software engineering concepts and 8+ years of applied AI/ML experience.
- Strong understanding of the Software Development Life Cycle (SDLC), Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Proficiency in RDBMS, NoSQL databases, and prompt design.
- Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, pyG, Keras, MXNet, and Scikit-Learn.
- Proficient in building AI Agents (e.g., LangChain, LangGraph, AutoGen), integration of tools (e.g., API), and RAG-based solutions (e.g., open search), Knowledge Graphs(e.g., neo4J).
- Proven track record of building and scaling software and/or machine learning platforms in high-growth or enterprise environments.
- Exceptional ability to communicate complex technical concepts to both technical and non-technical audiences.

Responsibilities

- Align ML problem definition with business objectives to ensure solutions address real-world needs.
- Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.
- Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.
- Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.
- Promote software and model quality, integrity, and security throughout the organization.
- Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.
- Integrate GenAI solutions with enterprise platforms using API-based methods.
- Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.
- Collaborate with technology teams to lead the design and delivery of GenAI products.

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