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Job Details

Skills

Market Risk
Jersey
Banking
Brand
Financial Services
Computer Science
Batch Processing
Docker
Kubernetes
High Performance Computing
C++
Python
Monte Carlo Method
Value At Risk
Performance Tuning
Conflict Resolution
Problem Solving
Communication
Quantitative Research
Trading
Risk Management
Amazon Web Services
Google Cloud Platform
Google Cloud
Finance
Orchestration
Distribution
CPU
GPU
Management
Version Control
Cloud Computing
Collaboration
GRID
Logistics
Market Analysis
Pricing
Recovery

Market Risk
Jersey
Banking
Brand
Financial Services
Computer Science
Batch Processing
Docker
Kubernetes
High Performance Computing
C++
Python
Monte Carlo Method
Value At Risk
Performance Tuning
Conflict Resolution
Problem Solving
Communication
Quantitative Research
Trading
Risk Management
Amazon Web Services
Google Cloud Platform
Google Cloud
Finance
Orchestration
Distribution
CPU
GPU
Management
Version Control
Cloud Computing
Collaboration
GRID
Logistics
Market Analysis
Pricing
Recovery

Summary

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
Required Skills & Experience

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 on massive-scale compute grids.
Expert-level, hands-on experience with at least one major public cloud provider (AWS or Google Cloud Platform), 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.
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.

What You Will Be Doing

Architect, build, and manage a massive-scale, distributed compute grid on public cloud platforms (AWS, Google Cloud Platform) 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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10105282
Position Id: 883274
Posted 2 days ago

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