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Senior Quantitative Developer – Market Risk, VaR & Financial Time Series

DeepData Inc.New York, NY🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationNew York, NY, United States
Apache SparkApacheDatabricksPythonAzureKubernetes

Job description

Senior Quantitative Developer – Market Risk, VaR & Financial Time Series

We are seeking an experienced Senior Quantitative Developer with strong hands-on expertise in market risk analytics, financial time series processing, and Python-based quantitative risk engine development. The ideal candidate will have a solid understanding of Value at Risk (VaR), Expected Shortfall (ES), stress testing, and quantitative modeling, along with the ability to develop scalable and efficient risk analytics solutions.

Responsibilities

The role will involve:

  • Design, develop, and maintain Python-based quantitative risk engines.
  • Process, analyze, and manage large-scale financial time series data.
  • Implement and enhance VaR and Expected Shortfall (ES) methodologies.
  • Develop and execute market risk stress testing and scenario analysis.
  • Support risk analytics aligned with FRTB and CCAR frameworks.
  • Optimize quantitative models, data pipelines, and risk calculations.
  • Collaborate with quantitative researchers, risk managers, and technology teams.
  • Ensure accuracy, performance, and reliability of risk analytics solutions.

Requirements

<>Required Skills
  • Strong hands-on experience in Python-based quantitative development.
  • Solid understanding of financial time series processing and analysis.
  • Practical experience with Value at Risk (VaR) and Expected Shortfall (ES).
  • Experience developing or maintaining market risk engines.
  • Knowledge of market risk stress testing and scenario analysis.
  • Strong analytical, quantitative, and problem-solving skills.
<>Preferred Skills
  • Familiarity with FRTB (Fundamental Review of the Trading Book).
  • Knowledge of CCAR (Comprehensive Capital Analysis and Review).
  • Experience with Azure cloud services.
  • Familiarity with Databricks, Apache Spark, and distributed data processing.
  • Exposure to Kubernetes and containerized application deployment.

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