Market Risk Time Series Analytics - Analyst/Associate

Market Risk Time Series Analytics Analyst

As a Market Risk Time Series Analytics Analyst in Risk Management and Compliance at JPMorgan Chase, you will be responsible for the development and implementation of analytics and the infrastructure used for Value at Risk (VaR) time series. You are central to keeping JPMorgan Chase resilient by anticipating emerging risks and applying expert judgment to solve challenges impacting our company, customers, and communities.

Our Risk Management and Compliance culture emphasizes innovation, challenging the status quo, and striving for best-in-class performance. In this role, you will develop methodologies and analytics for assessing and grading market data time series quality and remediating associated data quality issues.

Job Responsibilities

• Develop and enhance a robust analytics framework and infrastructure for market data time series and Average Daily Trading Volume data across multiple asset classes.
• Research and develop next-generation outlier and variance detection methodologies.
• Build outlier detection and missing data imputation tools, utilizing statistical tests, and analyzing their performance.
• Industrialize and automate the Average Daily Trading Volume production process.
• Design and develop a scalable framework that allows for easy onboarding of new data sources 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.
• Implement LLM analytics via MCP tool integrations, including guardrails and evaluation/monitoring for reliable outputs.
• Collaborate and liaise with Market Risk Coverage, Credit Risk, Product Specialists, and Technology partners.

Required Qualifications, Capabilities, and Skills

• Bachelor's or Master's degree in Statistics, Computer Science, Engineering, Quantitative Finance, or a related quantitative field.
• Expertise in Python, OOP knowledge is mandatory, plus experience with Numpy and Pandas.
• Ability to perform code optimization, debugging, and reverse engineering.
• Experience analyzing large and unstructured datasets, including handling distributed computing for large data processing.
• Knowledge of financial instruments and risk management principles (VaR, historical simulation, Monte Carlo, Greeks).
• Strong analytical and problem-solving skills, coupled with keen attention to detail and ownership of delivery.
• Ability to think critically and adapt to rapidly changing requirements.
• Excellent verbal and written communication skills and proficiency in technical documentation.
• Enthusiasm for knowledge sharing and the ability to collaborate effectively with cross-functional and global teams.

Preferred Qualifications, Capabilities, and Skills

• 0–4 years of relevant full-time experience within investment banking, hedge funds, asset management, or a related buy-side/sell-side financial institution.
• Familiarity with financial products such as equities, fixed income, FX, and commodities.
• Knowledge of front-end technologies (React, JavaScript, HTML) and integration with large datasets is a plus.
• Experience with prompt engineering for AI/LLM models and agentic workflow is preferred.
• Proficiency in Microsoft Excel, using advanced formulas and pivot tables.
• Credentials such as CFA or FRM are an added advantage.