Python Quantitative Developer

Global Financial Firm located inJersey City, NJ has an immediate contract opportunity for an experiencedPython Quantitative Developer.

Responsibilities:

• Architect and build a next-generation, hybrid-cloud platform for running quantitative pricing calculations at massive scale.
• Design a sophisticated job orchestration and scheduling system that handles dynamic prioritization, efficient task packing, and resource optimization across the grid.
• Engineer a robust, event-driven framework for managing the entire job lifecycle, including handling notifications for job completion and managing the flow of result data from the grid.
• Develop a comprehensive error-handling and resiliency strategy to gracefully manage failures in a large-scale distributed environment.
• Pioneer the development of an intelligent monitoring platform using ML to perform anomaly detection, predict job failures, and optimize grid performance.
• Engineer the secure and efficient data logistics required to move terabytes of market and trade data between on-premise systems and public cloud environments.
• Partner closely with front-office quants to onboard, version, and optimize their C and Python pricing models for distributed computation.

Requirements:

• Minimum 10 years of professional experience with a proven track record of designing and building applications for massive-scale, high-performance compute grids.
• A degree in a highly technical field such as Computer Science or Quantitative Finance.
• Deep domain knowledge in Capital Markets, financial derivatives, and a strong understanding of financial regulations like FRTB.
• Expert-level experience with job scheduling, resource management, and workload optimization techniques on large-scale grids.
• Deep expertise in building event-driven architectures and handling asynchronous notifications and replies from distributed systems.
• Proven experience designing and building intelligent monitoring tools, ideally leveraging ML for anomaly detection and performance tuning.
• Expert-level experience managing workloads in a hybrid cloud environment, with deep knowledge of at least one major public cloud (AWS, GCP) and on-premise grid technologies.
• Deep expertise in containerization, orchestration, and scheduling technologies (Docker, Kubernetes).
• Strong programming skills in languages common to high-performance and quantitative computing, such as C and Python.
• Proven ability to debug complex pricing and model-related issues by partnering directly with quantitative analysts.
• Prior experience in a similar role within the financial industry, specifically working on quantitative pricing platforms, large-scale risk calculations, or XVA grids.