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.

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At a glance

TL;DR · Quantitative Strategist, Associate

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As a quantitative strategist on the PWM Risk Strats team at Goldman Sachs Wealth Management, you will collaborate with risk management and fraud strategy teams to develop advanced ML models for fraud detection and business workflow optimization. Your daily tasks include delivering risk metrics, enhancing data-driven decision-making processes, and building robust analytical tools that support senior management and portfolio managers across multiple asset classes. You will leverage your expertise in mathematics, programming, and logical thinking to create innovative solutions that streamline risk management and improve operational efficiency. The role requires proficiency in Python (Pandas, NumPy, Scikit-learn), Spark, and SQL, with a preference for experience in machine learning techniques such as gradient boosting decision trees and random forests. This position offers the opportunity to work on cutting-edge financial technology challenges within a dynamic and fast-paced environment.

Skills

Python Pandas NumPy Scikit-learn Spark SQL Gradient Boosting Decision Trees Random Forests MCP LLM Models Time-Series Analysis Regression Analysis Object-Oriented Programming Machine Learning Risk Management Tools CI/CD

What you'll do

Develop and deploy ML models for fraud detection and business workflow enhancement.
Deliver risk metrics and quantitative analytics to manage financial and non-financial risks.
Create AI solutions to improve efficiency and accuracy in risk management processes.
Build and maintain systematic tools for risk management and reporting.
Design new strategies to address clients’ investment goals and optimize profitability.

What we're looking for

Bachelor's degree or higher in a quantitative field such as mathematics, physics, or computer science.
1-3 years of experience in quantitative financial modeling and software development.
Proficient programming skills with expertise in Python (Pandas, NumPy, Scikit-learn) and Spark.
Strong problem-solving abilities and creativity to develop innovative solutions.
Experience in machine learning techniques including gradient boosting decision trees and random forests.
Ability to work independently while collaborating effectively within a team environment.