This role pays more than
56%
of similar roles. Most pay
$151,375–$225,000
— the shaded band above.
At the midpoint, this role pays about
$194k
versus about
$188k
for comparable roles.
Based on 240 similar postings.
Employer
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
187 open roles
on FindRole.
Listed pay typically runs
$130,000–$250,000
across 60 roles with salary data.
As a Vice President in Quantitative Engineering at Goldman Sachs in New York, you will join the Global Banking and Markets team to develop sophisticated quantitative models and tools. Your daily responsibilities include conducting advanced econometric analyses, developing scalable data management systems, and supporting risk oversight processes. You will work on the full software development lifecycle, from requirements gathering through production support, focusing on multi-threaded applications in Unix environments and micro-services architecture using technologies like C++, Java, Python, Spring, REST, and SQL or NoSQL databases. This role requires expertise in Bayesian analysis, time series analysis, machine learning algorithms, and experience with the full software development lifecycle to address complex financial engineering challenges at a large scale.
Python
C++
Java
Bayesian analysis
Time series analysis
Machine learning algorithms
Linear Regression
Data management tools
Risk management
Scenario-based analysis
Statistics
Performance analysis
Econometric techniques
Python
C++
Java
Bayesian analysis
Time series analysis
Machine learning algorithms
Stochastic calculus
No-arbitrage pricing theory
Partial differential equations
Multivariable calculus
Linear algebra
Numerical methods
Optimization
Probability
Random processes
Risk management
Scenario-based analysis
Factor models
Data management tools
Python
C++
Java
TensorFlow
PyTorch
scikit-learn
Keras
Kubeflow
MLflow
Time Series Forecasting
MLOps
Big Data Technologies
Financial markets
Market making
Asset pricing
Machine Learning
Deep Learning
Large Language Models