Quantitative Developer​/Market Risk

Grow your career as a Quantitative Developer (Market Risk) with an innovative global bank in Jersey City, NJ. Contract role with strong possibility of extension. Will require working a hybrid schedule 3 days onsite per week.

Join one of the world's most renowned global banks and trusted brand with over 200 years of continuously evolving financial services worldwide. You will work alongside some of the smartest minds in the industry who are excited to share their knowledge and to learn from you.

Contract Duration: 6

Months

• A degree in Computer Science, Engineering, or a related technical field.
• 10+ years of professional experience with a proven track record of designing, building, and running applications onmassive-scale compute grids.
• Expert-level, hands-on experience with at least one major public cloud provider (AWSorGCP), 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 asC++and Python.
• 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.
• 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.

• 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.