Asset & Wealth Management - Quantitative Strategist - Associate - NYC

Responsibilities

As a strategist on our PWM Risk Strats team, you will work closely with various teams including risk management and fraud strategy. You will combine quantitative techniques and industry knowledge to build best‑in‑class models and tools that streamline risk management, detect fraud at scale, enable optimized data‑driven business decision making, and optimize profitability.

• Developing and deploying ML models for fraud and anomaly detection as well as business workflow enhancement
• Delivering risk metrics and quantitative analytics for financial and non‑financial risks across wealth management
• Develop AI‑led solutions to improve efficiency and accuracy in risk management.
• Building and maintaining robust and systematic risk management tools and reporting
• Collaborating on the design of new and existing strategies to address clients' investment goals.
• Developing and maintaining risk management and portfolio analysis tools across multiple asset classes for senior management and portfolio managers.
• Building and maintaining infrastructure of Strategists' analytical systems.

• Bachelor, Masters or Ph.D. in a quantitative or engineering field, e.g. mathematics, physics, quantitative finance, computational finance, computer science, engineering
• 1-3 years of experience in the job offered or related quantitative financial modeling and software development positions
• Programming and mathematical skills are required
• Creativity, problem‑solving skills, and ability to communicate complex ideas to a variety of audiences
• A self‑starter, should have ability to work independently as well as thrive in a team environment
• Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
• Experience with building models using common data science toolkits, i.e., Python (Pandas, Num Py, Scikit‑learn) and Spark
• Experience with prompt engineering, working with LLM models, and MCP.
• Previous work experience in:
  Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL

• Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
• Experience with building models using common data science toolkits, i.e., Python (Pandas, Num Py, Scikit‑learn) and Spark
• Experience with prompt engineering, working with LLM models, and MCP.
• Previous work experience in:
  Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL

The expected base salary for this New York, NY, United States‑based position is $115,000‑$180,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year‑end.

Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non‑temporary, full‑time and part‑time US employees who work at least 20 hours per week, can be found here.

Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity

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