Vice President, Quantitative Engineering

New York Times; New York (NY); On-site; USD 191,000 - 237,000; Full time

Job summary

Goldman Sachs is seeking a Vice President, Quantitative Engineering in New York to lead the design, development, implementation, and documentation of advanced quantitative models and scenarios for time series forecasting.

You will integrate economic, financial, and business-risk variables to address practical issues in finance and risk management and conduct uncertainty quantification through simulation and modern methods.

Qualifications

• PhD or Master’s or Bachelor’s in a quantitative field with required experience.
• High proficiency in statistical methods, ML, and time-series analysis.
• Experience with production-grade cloud deployment and data management.

Responsibilities

• Lead the design, development, and documentation of advanced quantitative models and scenarios for time series forecasting.
• Incorporate economic, financial, and business-risk variables to address practical finance issues.
• Perform uncertainty quantification using Monte Carlo and conformal prediction methods.
• Publish model risk documentation to support independent validation.

Skills

C++

Python

R

Econometrics

Time-series analysis

Explainable ML

Cloud deployment

Data management

Model validation

Education

PhD in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Master’s in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Bachelor’s in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Tools

SQL

Spark

Hadoop

Job description

Vice President, Quantitative Engineering with Goldman Sachs Services LLC in New York, New York. Lead the design, development, implementation, and documentation of advanced quantitative models and scenarios for time series forecasting. Incorporate economic, financial, and business-risk variables to address practical issues in finance and risk management and conduct uncertainty quantification.

Requirements

Requires:

• PhD degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics and one (1) year of experience in job offered or a related quantitative engineering role OR
• Master’s degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics and three (3) years of experience in job offered or a related quantitative engineering role OR
• Bachelor’s degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field such as Statistics and five (5) years of experience in job offered or a related quantitative engineering role.

Skills & Experience

• 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.

Job Code: 10427773.

Salary Range: Annual base salary for this New York, New York-based position is $191,000 - $236,800.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.

©The Goldman Sachs Group, Inc.,2026. All rights reserved.