Market Risk Time Series Analytics - Analyst at JPMorganChase · 545…

Summary: JPMorgan Chase is one of the oldest financial institutions, offering innovative financial solutions. The Market Risk Time Series Analytics Analyst is responsible for developing and implementing analytics and infrastructure for Value at Risk time series, focusing on data quality and analytics methodologies. Responsibilities: - Develop and enhance a robust analytics framework and infrastructure for market data time series and ADTV data for financial instruments across multiple asset classes - Research and develop next-generation outlier and variance detection methodologies - Build outlier detection and missing data imputation tools, employing statistical tests, and analyze their performance - Industrialize and automate the Average Daily Trading Volume production process - Design and develop a scalable framework that can easily onboard new data source while adapting to evolving analytics needs - Create, maintain and enhance APIs and statistical tools used for time series data management and visualization - Develop and implement front-end analytics and applications to deliver end-to-end market data solutions - Analyze large, unstructured datasets and perform statistical tests to assess data quality and tool performance - Design, develop, and optimize prompts for AI/LLM systems to retrieve and process relevant market data - Apply Retrieval-Augmented Generation (RAG) techniques to enhance data extraction and analytics using AI/LLM tools - Collaborate and liaise with Market Risk Coverage, Credit Risk, Product Specialists, and Technology partners Required Qualifications: - Advanced degree in Financial Engineering, Computer Science, or related quantitative field - Expertise in Python, OOP knowledge is a must, plus experience with Numpy and Pandas - Ability to perform code optimization, debugging, and reverse engineering - Strong analytical skills with a keen attention to detail - Experience analyzing large and unstructured datasets, handling distributed computing for large data processing - Knowledge of financial instruments and risk management principles (VaR, historical simulation, Monte Carlo, greeks) - Experience with prompt engineering for AI/LLM models - Ability to independently problem solve and take ownership for delivery - Ability to think critically and adapt to rapidly changing requirements - Excellent verbal/written communication skills and proficiency in technical documentation - Enthusiasm for knowledge sharing and ability to collaborate effectively with cross-functional and global teams Preferred Qualifications: - Experience with SDLC workflow and Athena environment for market risk model implementation - Knowledge of front-end technologies (React, JavaScript, HTML) and integration with large data sets - Understanding of Retrieval-Augmented Generation (RAG) concepts and practical application in data retrieval and analytics - Proficient in Microsoft Excel, using advanced formulas, pivot tables, etc - Ability to understand business processes and their risk implications, analyze complex situations, reach appropriate conclusions, and make feasible recommendations - Qualifications like CFA/FRM are an added advantage Required Skills: Python, Object-Oriented Programming, Numpy, Pandas, Code Optimization, Debugging, Reverse Engineering, Statistical Analysis, Large, Unstructured Data Analysis, Distributed Computing, Financial Instruments Knowledge, Risk Management Principles, Value at Risk (VaR), Historical Simulation, Monte Carlo Methods, Prompt Engineering for AI/LLM Models, API Development, Front-end Technologies, Retrieval-Augmented Generation (RAG), Analytical Skills, Greeks Benefits: Comprehensive health care coverage, On-site health and wellness centers, A retirement savings plan, Backup childcare, Tuition reimbursement, Mental health support, Financial coaching