Wholesale Credit Risk Associate - Product Strategy

Role overview

Wholesale Credit Risk Associate - Product Strategy at JP Morgan

Responsibilities

• Build robust data foundations that power analytics and intelligent applications.
• You will own delivery end-to-end across the software development life cycle, from requirements through support.
• You will conduct data exploration, analytics, and visualization to support analysis and exploration of large datasets.
• You will perform user acceptance testing and deliver demos to stakeholders using SQL queries and Python scripts.
• Ensure cross-platform data consistency and perform gap analysis using advanced software technologies.
• Perform user acceptance testing and deliver stakeholder demos using SQL queries and Python scripts.
• 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.
• Lead development activities by conducting regular stakeholder meetings, communicating timelines and blockers, and providing guidance to junior engineers and technologists.
• Build scalable data pipelines and trusted datasets in Databricks, delivering Tableau insights and stakeholder-ready demos.

Required qualifications

• Required qualifications, capabilities, and skills Education/experience: 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.

Preferred qualifications and skills

• Preferred qualifications, capabilities, and skills Experience performing statistical analysis to interpret data, identify trends, and draw conclusions using scikit-learn and TensorFlow.

Employment details

• Location: Mumbai [Maharashtra] [India]

Application details

• As a Data/AI Engineer, you will develop robust frameworks to extract data from multiple sources, apply business rules, and deliver comprehensive datasets for end-user consumption.

Additional role details

• You will develop scalable pipelines and consistent datasets across platforms, turning complex data into clear insights and visualizations.
• You will partner closely with stakeholders, perform user acceptance testing, and deliver demos using SQL and Python.
• You will ensure cross-platform data consistency and perform gap analysis using advanced software technologies.
• You will own all phases of the software development life cycle, including requirements gathering, design, development, testing, implementation, and support, with a focus on data models, metadata, and business transformation strategies.
• Job responsibilities Develop frameworks to extract data from multiple sources, apply business rules, and deliver comprehensive datasets for end-user consumption.
• 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.
• Identify hidden data problems and patterns and drive improvements in coding hygiene and system architecture.
• Incorporate prompt engineering and vector storage to enable intelligent, context-aware retrieval and response generation.
• Python proficiency, including Pandas and NumPy, with demonstrated experience developing and optimizing extract, transform, load (ETL) pipelines for data migration and processing.
• 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.
• Experience designing and managing scalable data pipelines using Airflow, Linux, and Git.
• Experience improving pipeline performance using parallelism, multi-threading, and batch processing techniques.