Wholesale Credit Risk Associate

About The Team

J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

Key Responsibilities

• Develop frameworks to extract data from multiple sources, apply business rules, and deliver comprehensive datasets for end-user consumption.
• Ensure cross-platform data consistency and perform gap analysis using advanced software technologies.
• Explore data and deliver analytics and visualizations, including insightful dashboards and reports in Tableau.
• Own end-to-end software development life cycle activities: requirements gathering, design, development, testing, implementation, and support.
• Define data models, metadata, and data dictionaries to enable thorough analysis and analytical explorations.
• Perform user acceptance testing and deliver stakeholder demos using SQL queries and Python scripts.
• Identify hidden data problems and patterns and drive improvements in coding hygiene and system architecture.
• Build and optimize scalable data pipelines, leveraging tools such as Databricks and PySpark for processing and analysis.
• Design and build end-to-end generative AI pipelines, including ingestion, preprocessing, model integration, and deployment workflows for large language model-powered applications.
• Incorporate prompt engineering and vector storage to enable intelligent, context-aware retrieval and response generation.
• Lead development activities by conducting regular stakeholder meetings, communicating timelines and blockers, and providing guidance to junior engineers and technologists.

Requirements & Tech Stack

• Python proficiency, including Pandas and NumPy.
• SQL proficiency, including window functions and creating stored procedures.
• Experience with Tableau to create dashboards and reports for analytics and visualization.
• Strong grounding in data engineering concepts, including ETL processes, data pipeline optimization, and data migration strategies.
• Experience with Oracle PL/SQL and Microsoft SQL Server for data management and analysis.
• Experience with AWS, Databricks, and PySpark for data processing and analysis.
• Experience building and optimizing end-to-end generative AI pipelines, including data preprocessing, large language model integration, prompt engineering, and deployment workflows using LangChain, LlamaIndex, or Hugging Face Transformers.
• Experience with retrieval-augmented generation architecture, including vector databases (Pinecone, ChromaDB, FAISS) and embedding models for context-aware retrieval systems.
• Experience leading development activities, conducting stakeholder meetings, and communicating timelines and blockers.

Qualifications

• Master's degree in Computer Science, Information Technology, Data Engineering, or related field; OR Bachelor's degree in the same fields plus 2 years of experience in the job offered or a similar technical role.
• Demonstrated experience developing and optimizing extract, transform, load (ETL) pipelines for data migration and processing.