JP Morgan Chase - Associate - Market Risk

The Market Risk E-Trading Risk & Models (ETRM) team is a Global team seeking a motivated and talented Associate to join the team; specifically, the part of ETRM that covers Global E-Trading, Models and Analytics for the Credit Trading and CCEM Markets, within the Corporate and Investment Bank (CIB). The candidate will focus on E-Trading activities, usage of valuation and E-Trading models, and development of data analytics within ETRM. The Global Credit Trading (GCT) and Commodities, Currencies and Emerging Markets (CCEM) team are part of the wider global ETRM team, spread across London, Mumbai, New York and Hong Kong.

The E-trading effort will cover ongoing review of existing and new E-Trading Activities, models (including the growth area of machine learning models) and E-Trading controls. The valuation models effort will include providing risk transparency by model, setting compensating controls for model limitations and working with Trading, Quantitative Research (QR) and Model Governance Group (MGG) to deploy new models in a controlled manner. The data analytics work will focus on liquidity analysis and markets microstructure, with additional project opportunities depending upon the Candidates proficiency within coding languages and appetite in this space.

The candidate will be expected to collaborate with Market Risk Coverage teams, partnering on strategic projects. Additionally, the candidate will work closely with Trading, Technology, Model Governance and Quantitative Research.

Qualifications:

- Bachelor's or Master's degree, ideally in a quantitative and/or financial field

- At least 4+ years of relevant or related experience

- The following are broad categories of skills that would be advantageous, but applications will be considered from a wide range of candidates:

- Familiarity with several asset classes

- Good understanding of financial products, including modelling techniques

- Good understanding of computer programming languages and coding techniques, such as R or Python

- Good understanding of Market Risk approaches, namely stress testing, scenario analysis, VaR, risk sensitivities, limits

- Some knowledge of Machine Learning approaches

- Experience with intra-day controls aimed to monitor electronic trading