At a Glance
- Tasks: Lead the development of advanced risk and pricing models for a top derivatives exchange.
- Company: CME Group is the world's leading derivatives marketplace, shaping global markets.
- Benefits: Enjoy a diverse workplace, career growth opportunities, and a chance to impact industries.
- Why this job: Join a team of experts and tackle complex challenges in quantitative risk management.
- Qualifications: Master's or Doctorate in relevant fields with 4-6+ years of experience in risk modelling.
- Other info: Embrace diversity and inclusion in a collaborative environment.
The predicted salary is between 43200 - 72000 Β£ per year.
CME Group is the world's leading and most diverse derivatives exchange. The role will be part of the CME Clearing Quantitative Risk Management department. Our Quants team are working with complex and advanced modelling and weβre looking for someone ready for a new challenge to join the Chicago team.
The Manager Quantitative Risk Management is responsible for developing Risk/Pricing Models that evaluate counterparty exposures to the Clearing House. These include models related to Pricing, Value-at-Risk, Stress Testing, Liquidity, Regulatory Capital, and also developing tools for Portfolio Analytics. The incumbent also works to develop strategies to perform back-testing to ensure the adequacy of margin coverage and model assumptions.
Principal Accountabilities:
- Conduct empirical studies and make recommendations on margin levels, modeling issues, and other risk-mitigation measures. Ensure that the model is up to date with the proven theories in the field.
- Design and develop pricing and risk models across different asset classes like Fixed Income Cash and Derivatives, OTC and Exchange-traded Futures and Options (e.g. Pricing, VaR, Backtest, Stress, Liquidity, etc.).
- Ensure risk models meet the risk appetite across varying needs for coverage, anti-procyclicality as well as provide transparency, replicability and what-if capabilities.
- Ability to do hands-on programming in C++/Java, SQL as well as Cloud-based platforms and work with financial developers and technology to deploy, test and continuously improve the models within the Production Infrastructure of CME.
- Document and present results to Sr. Management, Risk Committees as well as regulators and end clients; work with internal and external model validators for governance needs.
- These tasks apply at an individual contributor level, as well as a team supervisor and project manager because they entail mentoring junior quantitative and financial developers.
- For instance, the successful candidate will be ultimately responsible for the long-term modelling strategy, and for the architecture of the development library (supported by a quantitative developer).
Qualifications:
- Master or Doctorate in Computer Science, Financial Engineering, Financial/Applied/Pure Mathematics, Physics, or a related discipline.
- Academic skills: probability theory (including stochastic processes), statistics (time series analysis, process estimation), numerical methods (interpolation, integration, regression, root-finding, optimization, linear algebra, Monte-Carlo), Fixed Income financial mathematics.
- 4-6+ years of experience in pricing complex derivatives and performing advanced statistical analysis on underlying risk factors.
- Very strong expertise (3+ years) with Bond Mathematics, Fixed income Pricing and Risk modeling as well as with team management.
- 3+ years in developing risk models (e.g. Historical VaR, Monte Carlo VaR, Multi-Factor Risk Models, Stressed VaR, and Liquidity Risk models) as well as model evaluation techniques (backtesting, sensitivity analysis, coverage statistics, etc.).
- Experience providing theoretical justifications of risk models, for internal as well as external stakeholders. Also experience in developing risk model transparency and what-if analytics for risk managers, end users and regulatory stakeholders alike.
- Experience in writing model documentation and technical presentations.
The following would also be considered a plus:
- Experience in developing the type of risk models used by clearing houses and market risk teams.
- Experience with modern OO libraries, implementing pricing or risk frameworks.
Skills & Software Requirements:
- Proficiency in programming languages such as C++, Python, VBA and SQL is essential.
CME Group embraces our employees' diverse experiences, cultures and skills, and works to ensure that everyone's perspectives are acknowledged and valued. As an equal opportunity employer, we recognise the importance of a diverse and inclusive workplace and consider all potential employees without regard to any protected characteristic.
Contact Detail:
Job Traffic Recruiting Team
StudySmarter Expert Advice π€«
We think this is how you could land Manager Quantitative Risk Management
β¨Tip Number 1
Familiarise yourself with the latest trends in quantitative risk management and derivatives pricing. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
β¨Tip Number 2
Network with professionals in the field, especially those who work at CME Group or similar organisations. Attend industry conferences or webinars to make connections and gain insights that could give you an edge in your application.
β¨Tip Number 3
Brush up on your programming skills, particularly in C++, Python, and SQL. Being able to demonstrate your technical proficiency during discussions can significantly enhance your candidacy.
β¨Tip Number 4
Prepare to discuss your past experiences with risk modelling and statistical analysis in detail. Be ready to explain your thought process and the impact of your work, as this will showcase your expertise and problem-solving abilities.
We think you need these skills to ace Manager Quantitative Risk Management
Some tips for your application π«‘
Tailor Your CV: Make sure your CV highlights relevant experience in quantitative risk management, particularly in developing pricing and risk models. Emphasise your programming skills in C++, Python, and SQL, as well as any experience with financial mathematics.
Craft a Strong Cover Letter: In your cover letter, explain why you are passionate about quantitative risk management and how your background aligns with the responsibilities of the role. Mention specific projects or experiences that demonstrate your expertise in risk modelling and team management.
Showcase Your Technical Skills: Include a section in your application that details your technical skills, especially in programming languages and statistical analysis. Highlight any experience with back-testing and model evaluation techniques, as these are crucial for the role.
Prepare for Technical Questions: Be ready to discuss your previous work in detail, particularly any complex derivatives pricing or risk models you've developed. Prepare to explain your thought process and the methodologies you used, as this will likely come up during interviews.
How to prepare for a job interview at Job Traffic
β¨Showcase Your Technical Skills
Make sure to highlight your proficiency in programming languages like C++, Python, and SQL during the interview. Be prepared to discuss specific projects where you've applied these skills, especially in developing risk models or performing statistical analysis.
β¨Demonstrate Your Understanding of Risk Models
Familiarise yourself with various risk models such as Historical VaR, Monte Carlo VaR, and Liquidity Risk models. Be ready to explain how you have used these models in past roles and the theoretical justifications behind them.
β¨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about how you would approach margin level recommendations or back-testing strategies, and be ready to articulate your thought process clearly.
β¨Emphasise Team Leadership Experience
Since the role involves mentoring junior developers, be sure to discuss your experience in team management. Share examples of how you've successfully led a team, managed projects, and contributed to a collaborative work environment.