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

Posted 5 Days Ago
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New York, NY, USA
In-Office
191K-237K Annually
Senior level
Fintech • Financial Services
The Role
Lead design, development, validation, and cloud deployment of advanced time-series forecasting and explainable ML models for finance and risk. Build AI agentic systems, run simulation and uncertainty quantification, produce model risk documentation, and collaborate with business, finance, and risk stakeholders to translate needs into dashboards, reports, and compliant models.
Summary Generated by Built In

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.

Skills Required

  • PhD OR Master's OR Bachelor's in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, Statistics with required years of experience (PhD+1, Master's+3, Bachelor's+5).
  • Proficiency in programming languages: C++, R, Python.
  • Econometrics and Time-Series Analysis including modern forecasting techniques, structural-break analysis, and regime-switching analysis.
  • Simulation and Uncertainty Quantification including Monte Carlo simulation and Conformal Prediction methods.
  • Machine learning and non-parametric statistics expertise with emphasis on explainable ML, causal model selection, and hyperparameter tuning.
  • Production cloud deployment experience implementing mathematical and statistical models in scalable, production-grade cloud environments.
  • Data management and processing of large-scale structured and unstructured datasets using database query languages and data management tools.
  • Model validation and documentation experience including design/execution of simulation studies, theoretical justification, and producing model risk documentation for MRM validation.
  • AI agent development skills: agentic frameworks, context management, harness engineering, multi-agent orchestration, knowledge base integration, and safe code execution.
  • Experience translating complex user needs into model specifications, analytical metrics, dashboards, and reports; end-to-end model development lifecycle execution.

Goldman Sachs Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Goldman Sachs and has not been reviewed or approved by Goldman Sachs.

  • Healthcare Strength Coverage includes medical, dental, vision, disability, life and accident insurance, with multiple plan options and most premiums subsidized; coverage often starts on day one. Wellness resources, on-site health centers in some locations, and EAP access reinforce the depth of health support.
  • Parental & Family Support Family care includes on-site childcare in some offices, expectant parent resources, and transitional programs for returning parents. Feedback suggests parental leave is very generous, with reports of around 20 weeks paid leave and stipends for adoption, surrogacy, and fertility-related services.
  • Retirement Support The firm provides a 401(k) plan with employer matching contributions and broad financial education to help employees plan for retirement. Resources also support saving for education and preparing for unexpected events.

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The Company
HQ: New York, NY
67,118 Employees

What We Do

At Goldman Sachs, we believe progress is everyone’s business. That’s why we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, Goldman Sachs is a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices in all major financial centers around the world. More about our company can be found at www.goldmansachs.com

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