🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Role Overview JOB DESCRIPTION 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. 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 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. Minimum Education & Experience 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. 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. Special Skills Required To Perform The Job Prior experience (which can be fully satisfied through graduate-level academic research, coursework, or dissertation work for PhD candidates) must include 0 years with a PhD OR one (1) year with a Master’s OR three (3) years with a Bachelor’s with the following: Programming Languages: Rust, Python, or C++. (Rust is utilized primarily to accelerate scientific computing and may also be leveraged for agentic workflows). 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. Salary Range The expected base salary for this New York, New York, United States-based position is $110000-$130000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end. Benefits 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.