Engineering-New York-Vice President, Quantitative Engineering-10427773
$191,000–$236,800 year
On-siteNew York City, New York, United States or New York, United States
Job Summary
Lead the design, development, implementation, and documentation of advanced quantitative models for time series forecasting, incorporating economic and financial variables to address risk management issues. Develop and deploy explainable Machine Learning models for event prediction, derive actionable insights for business strategy and regulatory compliance, and collaborate with cross-functional stakeholders. Execute the end-to-end model development lifecycle, including data collection, feature engineering, hyperparameter tuning, and scalable cloud-based deployment. Design AI agentic systems for analytical capabilities, manage agent orchestration and knowledge base integration, and conduct rigorous simulation studies with performance testing. Create comprehensive technical documentation to support Model Risk Management reviews and ensure ongoing monitoring.
Required Qualifications
- PhD degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics
- one (1) year of experience in job offered or a related quantitative engineering role
- Master's degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics
- three (3) years of experience in job offered or a related quantitative engineering role
- Bachelor's degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics
- five (5) years of experience in job offered or a related quantitative engineering role
- programming Languages including C++, R, or Python
- econometrics and Time-Series Analysis including modern time-series econometric techniques for forecasting, structural-break analysis, and regime-switching analysis
- simulation and Uncertainty Quantification including Monte Carlo simulation and modern Conformal Prediction methods for uncertainty quantification
- machine Learning and non-parametric statistics including statistical learning methods with emphasis on explainable ML, causal model selection, and hyperparameter tuning
- production Cloud Deployment including implementation of mathematical and statistical models in scalable, production-grade cloud environments
- data Management including management and processing of large-scale structured and unstructured datasets using database query languages and data management tools
- model Validation and Documentation including design and execution of simulation studies, validation and theoretical justification, and production of comprehensive model risk documentation to support independent Model Risk Management (MRM) validation
- AI Agent Development including common agentic framework and context management, harness engineering, multi-agent orchestration, knowledge base integration, and safe code execution
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