J.P. Morgan Quantitative Research

Why complete this Job Simulation

A risk-free way to experience work on the job with us at J.P. Morgan. Practice your skills with example tasks and build your confidence to ace your applications.

Self-paced

6-7 hours

No grades

No assessments

Intermediate

Welcome to our program!

We are so excited to have you here. Quantitative Research (QR) is an expert quantitative modeling group at JPMorgan Chase, as well as a leader in financial engineering, data analytics, and portfolio management.

As a global team, QR partners with traders, marketers, and risk managers across all products and regions and contributes to sales and client interaction, product innovation, valuation, risk management, inventory and portfolio optimization, electronic trading and market making, and appropriate financial risk controls.

During this program, you will get the opportunity to step into the shoes of a JPMorgan Chase team member and complete tasks that replicate the work that our QR team does every day. You’ll learn key skills in data analysis, programming, and financial mathematics. We hope this program provides a great resource for you to upskill and strengthen your resume as you explore career options and a potential career at JPMorgan Chase Quantitative Research!

How it works

Complete tasks guided by pre-recorded videos and example answers from our team at J.P. Morgan. No live sessions, all self-paced.
Earn a certificate and add it to your resume and LinkedIn as an extra curricular activity.
Stand out in applications. Confidently answer interview questions and explain why you’re a good fit for our team.

Skills you will learn and practice:

Algorithm Development

Credit Analysis

Critical Thinking

Data Analysis

Financial Derivatives

Programming

Python Programming

Statistics

Tasks

Task One: Investigate and analyze price data

1-2 hoursIntermediate

Learn about commodities markets and demonstrate data analysis skills

What you'll learn

An overview of commodity storage contracts
How to extrapolate data from external feeds to provide granular insights

What you'll do

Write code that analyzes data to take a date as input and return a price for past and future estimates

Reviews

4.7

I liked how hands on the simulation was. Working with real datasets like natural gas pricing and loan default data made me feel like I was actually doing the work of a JPMorgan quant researcher. The step-by-step tasks and clear instructions helped me learn Python, data analysis, and financial modeling in a practical way. It was challenging but I enjoyed every bit of the journey.

Student, Nnamdi Azikiwe University

I really enjoyed this simulation because it provided practical experience in quantitative research and financial risk modeling. The tasks were realistic, well-structured, and helped me improve my Python, data analysis, and problem-solving skills while applying concepts used in the financial industry

Student, Université Ibn Zohr Agadir

The simulation was well structured and provided practical, hands-on experience with real-world quantitative research tasks. I particularly enjoyed working on data analysis, pricing commodity storage contracts, and building machine learning models for credit risk. It was a great opportunity to apply Python to realistic financial problems.

Student, Kurukshetra University