Market Risk Python Quant Engineer

• Take the sophisticated pricing models developed by our top quants and operationalize them on a colossal grid.
• Ensure that millions of trades can be re-valued against thousands of historical market scenarios with extreme speed efficiency and rock-solid stability.
• Architect the system that answers the most fundamental question in risk: What is it worth right now under this scenario The performance and reliability of the platform you build will directly determine the firms ability to manage risk and meet its most critical regulatory obligations.
• Architect build and manage a massive-scale distributed compute grid on public cloud platforms (AWS GCP) for running financial pricing models.
• Design and implement the orchestration layer responsible for distributing millions of pricing tasks efficiently across hundreds of thousands of CPU/GPU cores.
• Deploy manage and version control a diverse library of quantitative pricing models ensuring they run optimally in a distributed environment.
• Obsessively monitor and optimize the performance cost and resource utilization of the cloud grid driving continuous efficiency improvements.
• Collaborate with quantitative development teams to seamlessly integrate new and updated pricing models into the production grid.
• Engineer the data logistics to ensure that the correct market data trade data and model configurations are available for every calculation at runtime.
• Ensure the pricing engine is highly available resilient and capable of meeting stringent recovery time objectives.

• 10 years of professional experience with a proven track record of designing building and running applications on massive-scale compute grids.
• Expert-level hands-on experience with at least one major public cloud provider (AWS or GCP) including their batch processing container and serverless offerings.
• Deep expertise in containerization and orchestration technologies (Docker Kubernetes).
• Strong programming skills in languages common to high-performance computing such as C and Python.
• A degree in Computer Science Engineering or a related technical field.
• A strong background in distributed systems performance tuning and infrastructure-as-code principles.
• Exceptional problem-solving skills with an ability to diagnose and resolve complex issues in a high-pressure large-scale environment.
• Excellent communication skills and the ability to work effectively with quantitative research trading and risk management teams.

• Prior experience in a similar role within the financial industry (e.g. running large-scale Monte Carlo simulations VaR calculations or XVA pricing grids) is highly desirable.

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