This role pays less than
61%
of similar roles. Most pay
$147,048–$224,475
— the shaded band above.
At the midpoint, this role pays about
$170k
versus about
$186k
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
134 open roles
on FindRole.
Listed pay typically runs
$137,000–$250,000
across 55 roles with salary data.
The Associate, Quantitative Engineering role within the Global Banking & Markets division involves developing, implementing, and documenting complex scenarios involving diverse economic and financial variables. Working with internal stakeholders, you will analyze user needs from a scenario design perspective while addressing critical data, model, and implementation issues. You will process large structured and unstructured datasets to build predictive models of market variables, utilizing your knowledge of financial markets, economics, and statistical analysis. The role requires building and challenging risk models to identify vulnerabilities across market, credit, and liquidity risks. Key technical requirements include proficiency in C++, Java, or Python, alongside expertise in stochastic calculus, no-arbitrage pricing theory, and machine learning algorithms. You will also develop scalable data management tools and perform performance testing to provide robust risk oversight for the investment process.
Develop, implement, and document economic and financial scenarios for various business units.
Analyze large structured and unstructured datasets to build predictive models of market variables.
Refine and improve scenarios using knowledge of financial markets, economics, and statistical analysis.
Build and challenge risk models to identify vulnerabilities in market, credit, and liquidity risks.
Create and maintain technical documentation for risk-model performance testing processes.
Develop scalable data management and analysis tools to provide risk oversight.
Perform quantitative risk analytics including factor models and time series analysis.
What we're looking for
Master's degree in Financial Engineering, Economics, Mathematics, Data Science, Operations Research, or a related field and one year of relevant experience.
Bachelor's degree in Financial Engineering, Economics, Mathematics, Data Science, Operations Research, or a related field and two years of relevant experience.
Proficiency in C++, Java, or Python.
Experience developing probability and pricing models using financial mathematics principles like stochastic calculus, no-arbitrage theory, and linear algebra.
Experience in quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques including machine learning.
Experience performing risk management or scenario-based analysis.
Experience developing quantitative risk analytics, such as factor models.
Experience developing scalable data management tools and conducting statistics-driven performance analysis like Linear Regression or Time Series Analysis.
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
Application Security
Information Security Frameworks
Identity and Access Management (IAM)
Risk Management
SOX
Data Analysis
Artificial Intelligence
Cloud Platforms
Web Services
Distributed Systems
Control Testing