Corporate Planning & Management-New York-Senior Analyst-Quantitative Engineering

Goldman Sachs•New York, NY

5d•$110,000 - $130,000•Onsite

About The Position

As an Sr. Analyst Quantitative Strategist (Strat) within the CPM Strats team, you will focus on the design, development, and implementation of quantitative models to drive Budget Planning & Management. In this role, you will model and forecast revenues, expenses, and balance sheet dynamics. You will deploy scalable solutions on AWS Cloud and build secondary but core AI/agentic capabilities to streamline financial planning and analysis, with opportunities to leverage Rust to accelerate scientific computing. This position is at the Analyst level and is highly suited for recent graduates looking to apply advanced mathematical, statistical, and computational techniques to real-world corporate planning and financial forecasting challenges, and develop expertise developing AI agents for automated analysis.

Requirements

PhD degree (U.S. or foreign equivalent) in Statistics, Computer Science, Applied Mathematics, Physics or a related quantitative field. No prior professional work experience is required.
OR Master’s degree (U.S. or foreign equivalent) in Statistics, Computer Science, Applied Mathematics, Physics or a related quantitative field, and one (1) year of experience in the job offered or a related quantitative engineering role.
OR Bachelor’s degree (U.S. or foreign equivalent) Statistics, Computer Science, Applied Mathematics, Physics or a related quantitative field, and three (3) years of experience in the job offered or a related quantitative engineering role.
Programming Languages: Rust, Python, or C++.
Econometrics & Time-Series Analysis: Modern time-series econometric techniques for forecasting, structural-break analysis, and regime-switching analysis of financial metrics.
Simulation and Uncertainty Quantification: Monte Carlo simulation and modern Conformal Prediction methods for uncertainty quantification in financial planning.
Machine Learning and Non-Parametric Statistics: Statistical learning methods with emphasis on explainable ML, causal model selection, and hyperparameter tuning.
Production Cloud Deployment: Implementation of mathematical and statistical models in scalable, production-grade AWS Cloud environments.
Data Management: Management and processing of large-scale structured and unstructured datasets using database query languages (e.g., SQL) and data management tools.
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 graph-based orchestration, state and context management, tool integration, and safe execution environments.

Nice To Haves

PhD graduates with strong academic research backgrounds are highly preferred.
For non-PhD candidates, we value contributions to open source projects, publications, and other contributions that provide evidence of exceptional skill.

Responsibilities

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 explainable Machine Learning (ML) models for financial event prediction, revenue forecasting, and expense projection. 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 AWS 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, 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.
Develop, implement, and document scenarios comprised of a broad range of economic and financial variables for budget planning and management within the Firm.
Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues.
Analyze large datasets (structured and unstructured) to build predictive models of business-relevant financial variables (revenues, expenses, and balance sheet).
Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming.
Build and challenge revenue and expense models, identifying and quantifying vulnerabilities across financial planning and forecasting.
Create and maintain clear and complete technical documentation of the model performance testing approach and process.