Quant jobs / Goldman Sachs

Wealth Management Risk Quantitative Strategist – Associate

A strats role in the Private Wealth Management risk team, building machine learning models for fraud detection and risk analytics. It also covers portfolio analysis tools for managers.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Build and deploy ML models for fraud and anomaly detection
Deliver risk metrics for financial and non-financial risks across wealth management
Develop AI-driven tools that make risk management more efficient
Maintain risk and portfolio analysis tools across asset classes
Maintain the strategists' analytical infrastructure

What they ask for

Required

Programming and maths skills
Python with Pandas, NumPy, scikit-learn and Spark
Time-series and regression analysis
SQL and relational databases
Object-oriented programming
Communication

Preferred

Gradient boosting and random forests
Prompt engineering, LLMs and MCP

Education: Bachelor's, Master's or PhD in a quantitative or engineering field
Experience: 1–3 years in quantitative financial modelling or software development
Languages and tools: Python
Spark
SQL
Markets: multi-asset

How to prepare

Train a tree-based anomaly or fraud detection model on a public imbalanced dataset and evaluate it carefully
Build a portfolio risk report in Python from holdings data
Practise time-series and regression diagnostics
Try an LLM-based workflow for summarising risk reports and note where it fails

What to study

The Risk Quant roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Goldman Sachs’s site before applying: requirements, location and pay can change after we check them.

View the posting on Goldman Sachs’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Goldman Sachs and doesn’t handle applications.