below marketmost similar roles pay hereabove market
This role pays less than
63%
of similar roles. Most pay
$133,800–$188,125
— the blue band above.
At the midpoint, this role pays about
$148k
versus about
$161k
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
71 open roles
on FindRole.
Listed pay typically runs
$140,000–$250,000
across 34 roles with salary data.
The Associate Quantitative Strategist joins the Core Planning and Analysis Strats team to design, develop, and implement quantitative models for Budget Planning and Management. This role involves modeling and forecasting revenues, expenses, and balance sheet dynamics while engineering AI agents to automate analysis and reporting across the planning lifecycle. Key responsibilities include executing the end-to-end model development lifecycle, conducting uncertainty quantification, and performing rigorous simulation studies. Candidates must utilize Python for cloud deployment, with potential use of Rust for performance-critical scientific computing. Required skills include econometrics, time-series analysis, explainable machine learning, and causal inference. The position focuses on solving corporate planning challenges by translating complex needs into interactive dashboards and autonomous agentic systems using frameworks like LangGraph, Google ADK, or AWS Bedrock AgentCore.
Design and implement quantitative models for time-series forecasting of revenues, expenses, and balance sheet items.
Develop and deploy explainable machine learning models for event prediction and corporate strategy insights.
Engineer AI agentic systems to automate analysis, reporting, and decision support across the planning lifecycle.
Execute the end-to-end model development lifecycle including data collection, feature engineering, and hyperparameter tuning.
Deploy scalable mathematical and statistical models in production-grade Cloud environments using Python and Rust.
Conduct rigorous simulation studies and uncertainty quantification using Monte Carlo and Conformal Prediction methods.
Create technical documentation to support Model Risk Management reviews and ensure ongoing model monitoring.
What we're looking for
Degree in Statistics, Computer Science, Applied Mathematics, Physics, or a related quantitative field.
PhD graduates with strong academic research backgrounds are highly preferred.
Strong proficiency in Python.
Experience with or interest in developing Rust or C++ for performance-critical numerical code (preferred).
Proficiency in modern econometric and time-series methods for multivariate forecasting and economic scenario generation.
Experience with Monte Carlo simulation and modern Conformal Prediction methods for uncertainty quantification.
Experience with explainable ML, non-parametric statistical learning, principled model selection, and hyperparameter tuning.
Experience with causal model selection, treatment-effect estimation, and counterfactual analysis.
Experience implementing mathematical and statistical models in scalable, production-grade Cloud environments.
Experience designing and implementing autonomous agentic systems and multi-agent workflows using frameworks like LangGraph, Google ADK, or AWS Bedrock AgentCore.
Python
SQL
ETL
Tableau
Alteryx
PowerBI
R
Java
C++
Data Visualization
Business Intelligence
Statistics
Time Series Analysis
Numerical Algorithms
Data Analysis