Market Risk Time Series Analytics - Analyst/Associate

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 120,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Market Risk Time Series Analytics Analyst to design and implement analytics and infrastructure for VaR time series across multiple asset classes.

The role emphasizes data quality, outlier detection, and scalable data pipelines, with collaboration across Risk Management, Credit Risk, and Technology teams. The candidate will industrialize production processes for market data analytics, develop APIs, and advance front-end analytics applications, leveraging Python,

Qualifications

• Bachelor's or Master's degree in Statistics, Computer Science, Engineering, Quantitative Finance or related quantitative field.
• Expertise in Python, OOP, plus experience with NumPy and Pandas.
• Ability to perform code optimization, debugging, and reverse engineering.
• Experience analyzing large and unstructured datasets, handling distributed computing for large data processing.

Responsibilities

• Develop and enhance a robust analytics framework and infrastructure for market data time series and Average Daily Trading Volume 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.
• Implement LLM analytics via MCP tool integrations, guardrails, and evaluation/monitoring for reliable outputs.
• Collaborate and liaise with Market Risk Coverage, Credit Risk, Product Specialists, and Technology partners.

Skills

Python

NumPy/Pandas

Data analysis

Distributed computing

Documentation

Communication

Education

Bachelor's or Master's in Statistics/CS/Engineering/Quantitative Finance

Tools

React

JavaScript

HTML

Job description

As a Market Risk Time Series Analytics Analyst in Risk Management and Compliance at JPMorgan Chase, you are specifically responsible for the development and implementation of the analytics and the infrastructure used for VaR (Value at Risk) time series. You are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers, and communities.

Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo, and striving to be best-in-class. As an Market Risk Time Series Analytics Analyst you are specifically responsible for the development and implementation of the analytics and the infrastructure used for VaR (Value at Risk) time series. The team develops methodologies and analytics for assessing and grading the quality of the market data time series and for remediating 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 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.
• Implement LLM analytics via MCP tool integrations, 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 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.
• 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).
• Strong analytical skills and problem solving skills with a keen attention to detail 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, 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 (e.g., equities, fixed income, FX, commodities).
• Knowledge of front-end technologies (React, JavaScript, HTML) and integration with large data sets is a plus.
• Experience with prompt engineering for AI/LLM models and agentic workflow
• Proficient in Microsoft Excel, using advanced formulas, pivot tables, etc.
• Qualifications like CFA/FRM are an added advantage.