Job Summary:
If you are passionate, curious, and ready to make an impact, we are looking for you.
Quantitative Research (QR) is an expert quantitative modeling group in J.P. Morgan, as well as a leader in financial engineering, data analytics, statistical modeling, and portfolio management. As a global team, QR partners with traders, marketers, and risk managers across all products and regions, contributes to sales and client interactions, product innovation, valuation and risk management, inventory and portfolio optimization, electronic trading and market making, and appropriate financial risk controls.
We are looking for a talented Quant to join the Quantitative Research Securities Services global team. The role is based in London. The Securities Services business holds $27tn of Assets under Custody (AUC) and more than $200bn of cash, providing opportunities for Quantitative Research to add value through innovative use of data analytics and machine learning.
You will be a part of a team that is helping to transform business practices though data science and other quantitative methods where JP Morgan is already a dominant player, handling trillions of dollars of client assets. Your first responsibility will be to contribute to various machine learning projects.
In addition, we are providing on job training, intensive internal classroom training, and online courses, all given by our experienced quants. Through the diversity of the businesses it supports, and the variety of functions that it is responsible for, the Quantitative Research provides unique growth opportunities for you to develop your abilities and your career.
Job responsibilities:
- Lead machine learning projects from research stage into production deployment with a state-of-the-art Python development environment.
- Make real-world, commercial recommendations through effective presentations to various stakeholders.
- Leverage data visualization to communicate quantitative insights to help business decision making.
Required qualifications, capabilities, and skills:
- Masters/Ph.D. or equivalent in a quantitative field (Quantitative Finance, Statistics, Mathematics, Computer Science, Physics, Economics, ...).
- Strong Programming experience in Python: pandas, numpy, scikit-learn, tensorflow, SDLC, clean code theory
- Machine learning knowledge in supervised learning models such as Random Forests, boosted trees, and neural networks
- Strong communication skills (both verbal and written) and the ability to present findings to a non-technical audience.
- Participation in Kaggle competitions or contributions to GitHub are a plus
Beyond the above we are interested in what makes you unique: personal qualities, interests, and achievements that demonstrate who you are and the differences you will bring to the team.