The Core Engineering-Associate, Quantitative Strategist, Core Planning and Analysis Strats

Goldman Sachs · New York City · $115,000–$180,000 (stated in the posting)

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Role Overview

As an Associate Quantitative Strategist (Strat) within the Core Planning and Analysis Strats team, you will focus on two complementary mandates: (1) the design, development, and implementation of quantitative models to drive Budget Planning & Management — modeling and forecasting revenues, expenses, and balance sheet dynamics — and (2) the design and engineering of AI agents to automate analysis, reporting, and decision support across the planning lifecycle. You will build and deploy scalable solutions in the Cloud, primarily in Python, with opportunities to contribute to our growing adoption of Rust for performance-critical scientific computing.

This position is at the Associate level and is highly suited for recent PhD graduates looking to apply advanced mathematical, statistical, and computational techniques to real-world corporate planning and financial forecasting challenges, and to develop deep expertise in building AI agents for automated analysis.

Job Duties

• Design, develop, implement, and document advanced quantitative models and scenarios for time-series forecasting of revenues, expenses, and balance sheet items. Incorporate a broad range of economic, financial, and business variables to address practical issues in budget planning and management, and conduct uncertainty quantification.
• Develop and deploy Statistical and explainable Machine Learning (ML) models for event prediction and forecasting. Derive actionable insights to support corporate strategy, budget planning, regulatory compliance, and internal governance reviews.
• Collaborate with cross-functional stakeholders across business divisions, Finance, Risk, and other core corporate departments. Translate complex user needs into precise model specifications, analytical metrics, interactive dashboards, and comprehensive reports tailored for senior leadership and operational teams.
• Execute the end-to-end model development lifecycle, encompassing data collection, exploratory data analysis, feature engineering, variable selection, model selection, hyperparameter tuning, validation, and scalable deployment on the Cloud.
• Design and engineer Artificial Intelligence (AI) agentic systems to deliver analytical, data science, and reporting capabilities through both interactive and batch reporting interfaces. Manage agent orchestration, context management, knowledge base integration, tool calling, and overall AI lifecycle management.
• Conduct rigorous simulation studies, provide theoretical justifications, and perform model performance testing. Create and maintain comprehensive technical documentation to support Model Risk Management (MRM) reviews, facilitate finding remediation, and ensure ongoing model monitoring.

Minimum Education & Experience Requirements

Required field of study (U.S. or foreign equivalent, for all paths below): Statistics, Computer Science, Applied Mathematics, Physics, or a related quantitative field.

PhD graduates with strong academic research backgrounds are highly preferred, but we will also consider experienced Masters and Bachelors. We value contributions to open source projects, publications, and other work and activities that provide evidence of exceptional ability.

Special Skills Required to Perform the Job

Prior experience — satisfied through professional work or, for PhD candidates, graduate-level research, coursework, or dissertation work — must demonstrate the following:

• Programming Languages: Strong proficiency in Python. Experience with — or interest in developing — Rust (or C++) for performance-critical numerical code is a plus and aligns with the team's strategic direction.
• Econometrics & Time-Series Analysis: Modern econometric and time-series methods for multivariate forecasting and economic scenario generation, including state-space models, VAR/VECM and cointegration analysis, Bayesian VAR and dynamic factor models, structural identification, and nonlinear/regime-switching models.
• Simulation and Uncertainty Quantification: Monte Carlo simulation and modern Conformal Prediction methods for uncertainty quantification.
• Machine Learning: Explainable ML, non-parametric statistical learning, principled model selection, and hyperparameter tuning.
• Causal Inference: Causal model selection and identification, treatment-effect estimation, instrumental variables, and counterfactual / what-if analysis.
• Production Cloud Deployment: Implementation of mathematical and statistical models in scalable, production-grade Cloud environments.
• AI Agent Development: Design and implementation of autonomous agentic systems and multi-agent workflows using frameworks such as LangGraph, Google ADK, or AWS Bedrock AgentCore, including orchestration, state/context management, tool integration, and safe execution.

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