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

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

Job summary

Goldman Sachs Services LLC in New York, NY seeks a Vice President, Quantitative Engineering to lead design, development, and deployment of advanced quantitative models and scenarios for time series forecasting, incorporating economic and business-risk variables.

You will drive end-to-end model development, validation, and documentation for Model Risk Management, build explainable ML models for event prediction, and collaborate with risk and finance teams across the firm.

Qualifications

• PhD or Master's or Bachelor's in a quantitative field with related experience.
• Programming languages include C++, R, Python; econometrics and time-series analysis; Monte Carlo simulation and conformal prediction; production cloud deployment; data management; model validation and documentation; AI agent development.

Responsibilities

• Lead design and implementation of advanced quantitative models and scenarios for time series forecasting.
• Develop and deploy explainable ML models for event prediction and risk scoring.
• Conduct end-to-end model development lifecycle from data collection to cloud deployment.
• Create and maintain technical documentation for Model Risk Management reviews.

Skills

C++

R

Python

Time-series analysis

Econometrics

Uncertainty quantification

Explainable ML

Cloud deployment

Data management

Model validation

AI agent development

Education

PhD in Mathematics/CS/Financial Engineering/Applied Mathematics or Statistics

Master's in Mathematics/CS/Financial Engineering/Applied Mathematics or Statistics

Bachelor's in Mathematics/CS/Financial Engineering/Applied Mathematics or Statistics

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

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