Vice President, Quantitative Engineering

Goldman Sachs

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: New York, NY
Salary: $191,000–$236,800 / yr
Posted: 31 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $212k

This role $214k

$140k most similar roles pay here $267k

This role pays more than 59% of similar roles. Most pay $168,750–$254,943 — the shaded band above. At the midpoint, this role pays about $214k versus about $212k for comparable roles.

Based on 240 similar postings.

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About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 134 open roles on FindRole.

Listed pay typically runs $137,000–$250,000 across 55 roles with salary data.

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At a glance

TL;DR · Vice President, Quantitative Engineering

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Engineering-New York-Vice President, Quantitative Engineering-10427773 joins the Engineering Division to lead the design, development, and documentation of advanced quantitative models for time series forecasting. The role involves incorporating economic and risk variables to address practical issues in finance and risk management while conducting uncertainty quantification. You will build explainable Machine Learning models for event prediction, develop AI agentic systems with conversational interfaces, and manage agent orchestration and knowledge base integration. Key responsibilities include executing the end-to-end model development lifecycle, from feature engineering to scalable cloud deployment. Required skills include proficiency in C++, R, or Python; expertise in econometrics, Monte Carlo simulation, and Conformal Prediction; and experience with non-parametric statistics. The position focuses on solving complex problems in risk scoring, regulatory compliance, and internal governance through rigorous simulation studies and comprehensive technical documentation for Model Risk Management reviews.

Skills

Python C++ R Machine Learning Time-Series Analysis Monte Carlo Simulation Conformal Prediction Feature Engineering Model Risk Management Econometrics Statistical Learning Hyperparameter Tuning Exploratory Data Analysis

What you'll do

Design and implement advanced quantitative models for time series forecasting using economic and risk variables.
Develop and deploy explainable machine learning models for event prediction and risk scoring.
Execute the end-to-end model development lifecycle including data collection, feature engineering, and hyperparameter tuning.
Engineer AI agentic systems to provide analytical capabilities through conversational interfaces.
Manage AI agent orchestration, context management, and knowledge base integration.
Conduct rigorous simulation studies and perform model performance testing with theoretical justifications.
Create comprehensive technical documentation to support Model Risk Management reviews and compliance.
Translate complex user needs into precise model specifications, interactive dashboards, and analytical reports.

What we're looking for

A PhD in a quantitative field with experience, a Master's with , or a Bachelor's with is required.
Proficiency in programming languages including C++, R, or Python is required.
Expertise in econometrics and time-series analysis, including structural-break and regime-switching analysis, is required.
Experience in simulation and uncertainty quantification using Monte Carlo methods and Conformal Prediction is required.
Knowledge of machine learning and non-parametric statistics with a focus on explainable ML and causal model selection is required.
Experience in production cloud deployment for mathematical and statistical models in scalable environments is required.
Ability to manage large-scale structured and unstructured datasets using database query languages and data management tools is required.
Experience in AI agent development, including multi-agent orchestration and knowledge base integration, is required.