Market Risk Time Series Analytics - Analyst

JPMorganChase•Jersey City, NJ

About The Position

Bring your Expertise to JPMorgan Chase. As part of Risk Management and Compliance, 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.

Requirements

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.

Nice To Haves

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.

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.