BNP Paribas is a leading bank in Europe with an international reach. It has a presence in 73 countries, with more than 196,000 employees, including around 149,000 in Europe. The Group has key positions in its three main activities: Domestic Markets, International Financial Services (whose retail-banking networks and financial services are covered by Retail Banking & Services) and Corporate & Institutional Banking, which serves two client franchises: corporate clients and institutional investors.
BNP Paribas Corporate and Institutional Banking is a globally recognised leader offering capital markets, securities services, financing, treasury and advisory solutions.
Business Area/Dept Overview
SIGMA is the quantitative modelling team with overall responsibility for market, liquidity and counterparty credit risk methods within BNP Paribas.
The team sits within RISK Models and Regulatory, which is part of the RISK Function of the group. The RISK Function is globally accountable for the definition of official risk policies, guidelines and procedures, as well as the quantification and monitoring of risks taken by the various business lines, to ensure alignment with risk appetite and policies. At BNP Paribas, a well-developed risk management culture is based on a long-term vision, a committed management, and a strong and independent organisation.
SIGMA’s mission is to develop and continually improve the Group’s risk modelling & measurement, analysis and back-testing capabilities. SIGMA is organised in four streams, each responsible for a given asset class (IRFX, Credit / Repo, Equity / Commodity) or transversal aspects of risk methods (Cross-Product), supported by architects responsible for ensuring consistency across methodological research and development activities.
The team’s remit includes all the IMM models in use within the Bank, such as VaR, Stressed VaR, IRC and CRM models in the market risk space, as well as EEPE, Stressed EEPE, Regulatory CVA models in the counterparty risk space. In the context of market risk modelling, the incoming regulation surrounding the “Fundamental Review of the Trading Book” (FRTB) is becoming an increasingly important cornerstone for the team.
Purpose & Scope of role
Carrying out quantitative analyses and developments as laid out in the team’s mission statement
Global responsibility for the Group, in line with SIGMA’s team mandate; within SIGMA, the sub-team responsibility comprises a given asset class (e.g. equity/commodity) or function (e.g. methodology development architecture)
Key Responsibilities of role
The primary focus will be on the creation, maintenance, documentation and testing of algorithmic code, but the role requires a solid quantitative background and a strong interest in this aspect. The work scope comprises the full range of risk measurement methods in the team’s remit, across asset classes and across counterparty risk and market risk perimeters. Examples include counterparty risk models, pricers, model calibrations and backtesting models, as well as market risk simulations and pricing methods.
The role requires a contribution to both methods and system requirements and design rather than just implementation of a detailed technical specification. In this respect, an active contribution to methods, system requirements and design discussions is expected; where relevant, complemented by challenging both implementation details and design decisions by tracing these back to the business requirements.
Whilst the role may involve all aspects of the team-wide responsibilities, the candidate will specifically contribute to the initiatives within the Architecture stream of SIGMA.
Experience, Qualifications & Competencies
To be successful in this role, the candidate should meet the following requirements:
- A strong academic background, with at minimum a Masters in mathematics, physics or quantitative finance or equivalent relevant experience ;
- Design and implementation of quantitative models, using C#, Java or C++ (and object-oriented programming in general), in a source-controlled environment (e.g. Git);
- Understanding of large production systems in some detail, both from algorithmic and performance perspectives; knowledge of continuous testing and integration.
In addition, the following are desirable:
- Proven experience in a quantitative finance environment, preferably in a market or counterparty risk model development capacity – knowledge of asset simulation and stochastic model;
- Practical knowledge of derivatives, their risk drivers and pricing models;
- Exposure to one of the following asset classes: credit, repo, IR/FX, equity, commodities;
- Good grasp of the current regulatory framework and its implications on banks’ operations.
This role will expose the candidate to a wide range of professionals within the bank. Therefore, communication skills, both written and verbal, play an essential part of the day-to-day role.
In addition, the candidate will have the ability to:
- Work to meet tight deadlines;
- Work flexibly as part of multiple teams and autonomously;
- Grasp the intricacies of governance-related processes and procedures;
- Juggle changing priorities and a varied workload.