Python Quantitative Developer

Global Financial Firm located in Jersey City, NJ has an immediate contract opportunity for an experienced Python Quantitative Developer. Pay Rate: $91.00 - $96.00 per hour Work Mode: Hybrid Location: Jersey City, NJ 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.