Engineering-New York-Vice President, Quantitative Engineering-10427773

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

Job summary

Goldman Sachs seeks 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 incorporate economic, financial, and business-risk variables to address practical issues in finance and risk management, and conduct uncertainty quantification.

Develop and deploy explainable ML models for event prediction, risk scoring, and governance reviews.

Qualifications

• PhD/MS/BS in Mathematics, CS, Financial Engineering, or related quantitative field with required years of experience.
• Programming in C++, R, or Python as part of the offered role.
• Experience in econometrics and time-series analysis for forecasting and uncertainty quantification.

Responsibilities

• Lead design, development, and deployment of quantitative models and ML solutions.
• Perform end-to-end model lifecycle: data collection, feature engineering, model selection, validation, and deployment.
• Support Model Risk Management with documentation and governance reporting.
• Collaborate with cross-functional stakeholders across Finance and Risk.

Skills

C++

Python

R

Time-series analysis

Econometrics

Machine Learning

Uncertainty quantification

Cloud deployment

Data management

Model validation

AI agent development

Education

PhD in a quantitative field

MS in a quantitative field

BS in a quantitative field

Tools

SQL

Git

Job description

Job Duties

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. Develop and deploy explainable Machine Learning (ML) models for event prediction and risk scoring. Derive actionable insights to support business strategy, regulatory compliance, and internal governance reviews. Collaborate with cross-functional stakeholders across business divisions, Finance and risk departments. Translate complex user needs into precise model specifications, analytical metrics, interactive dashboards, and comprehensive reports. Execute the end-to-end model development lifecycle, encompassing data collection, exploratory data analysis, feature engineering, variable selection, model selection, hyperparameter tuning, validation, and scalable cloud-based deployment. Design and engineer Artificial Intelligence (AI) agentic systems to deliver analytical, data science, and reporting capabilities through conversational interfaces. Manage agent orchestration, context management, knowledge base integration, and overall AI lifecycle management. Conduct rigorous simulation studies, provide theoretical justifications, and perform model performance testing. Create and maintain comprehensive technical documentation to support Model Risk Management reviews, facilitate finding remediation, and ensure ongoing model monitoring.

Job Requirements

• 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. Prior experience must include one (1) year with PhD OR three (3) years with Master’s OR five (5) years with Bachelor’s with the following: 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; and AI Agent Development including common agentic framework and context management, harness engineering, multi-agent orchestration, knowledge base integration, and safe code execution.

Salary

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

Equal Opportunity

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.