Quantitative Strategy Associate AI Forecasting & Planning

The Goldman Sachs Group; New York (NY); On-site; USD 100,000 - 130,000; Full time

Job summary

The Goldman Sachs Group in New York seeks an Associate Quantitative Strategist to enhance Budget Planning and AI development. This role emphasizes designing scalable quantitative models, focusing on data-driven decision-making, and fostering collaboration with business divisions to deploy analytical solutions.

Ideal candidates will possess a PhD in relevant fields with a strong background in Python, Machine Learning, and econometrics, alongside experience in AI agent systems. This position offers exposure to advanced financial forecasting challenges, perfect for individuals eager to apply theoretical knowledge in real-world scenarios.

Qualifications

• Strong proficiency in Python and experience with Rust or C++.
• Knowledge of econometric methods and time-series analysis.
• Experience with machine learning and causal inference.

Responsibilities

• Design and implement quantitative models for forecasting.
• Develop and deploy Machine Learning models for predictions.
• Collaborate with stakeholders to translate user needs into models.

Skills

Python

Machine Learning

Econometrics

Time-Series Analysis

Simulation and Uncertainty Quantification

AI Agent Development

Education

PhD in Statistics, Computer Science, Applied Mathematics, or Physics

Master’s or Bachelor's degree

Tools

Rust

C++

AWS Bedrock AgentCore

Job description

The Goldman Sachs Group in New York seeks an Associate Quantitative Strategist to enhance Budget Planning and AI development. This role emphasizes designing scalable quantitative models, focusing on data-driven decision-making, and fostering collaboration with business divisions to deploy analytical solutions.

Ideal candidates will possess a PhD in relevant fields with a strong background in Python, Machine Learning, and econometrics, alongside experience in AI agent systems. This position offers exposure to advanced financial forecasting challenges, perfect for individuals eager to apply theoretical knowledge in real-world scenarios.