Job Description

The Core Engineering 

The Core Engineering builds and operates the platforms, applications, data solutions, models, and analytics that power critical processes for The Core divisions of the firm (e.g., Risk, responsible for the risk profile of firm activities; Controllers, responsible for the financial control and reporting obligations; Compliance, responsible for the firms compliance, regulatory, and reputational risks; Corporate Treasury, responsible for the firms liquidity, funding, balance sheet, etc.; and Human Capital Management, responsible for attracting, developing, and managing a global workforce). A centralized engineering structure in support of The Core enables a common platform model and operating framework that promotes consistent governance and scalable solutions, leveraging cloud, AI, and machine learning for innovation and efficiency. The Core Engineerings 2,000+ engineers and strats deliver engineering, data, analytics, and quantitative capabilities within six business units: 

• Metrics & Analytics Platforms: responsible for the measurement and management of the firms risk, capital, and liquidity for The Core functions
• The Core Strats: responsible for the development and implementation of models and other quantitative methodologies, including the accuracy and attribution of modeled metrics
• Financials & Reporting: responsible for facilitating the production of the firms financials and a wide range of reporting functions
• Non-Financial Risk & Controls: responsible for non-financial risk and control processes
• Enterprise Platforms: responsible for platforms and applications that support critical operational processes across The Core such as payments, people processes, and procurement
• Shared Services: responsible for driving the adoption of consistent engineering strategy, including data platforms, cloud, and AI enablement, as well as the management of technology risk 

Planning & Analysis Strats | Budget Planning, Scenario Design and Risk Analytics

Role Overview

As an Associate Quantitative Strategist (Strat) within the Planning & Analysis Strats team, you will design, develop and implement quantitative models and macroeconomic and financial scenarios supporting budget planning and management, firm-wide scenario-based risk management and regulatory stress testing. The role combines econometric modeling, financial forecasting, machine learning and large-scale data analysis to project revenues, expenses and balance sheet dynamics, quantify uncertainty and identify vulnerabilities. You will build scalable solutions on AWS Cloud and develop AI and agentic capabilities to streamline scenario analysis, model development and reporting. You will work with stakeholders across business divisions, Finance, Risk and other corporate functions to translate varied use cases into robust scenarios, models and decision-ready analysis.

This Associate-level position is well suited to recent PhD graduates seeking to apply advanced mathematical, statistical, econometric and computational methods to financial planning, scenario design and risk-management challenges.

Job Duties and Responsibilities

• Research, design, build and implement quantitative models and macroeconomic and financial scenarios for budget planning, risk management and regulatory stress testing, with clear supporting documentation.
• Develop statistical, econometric, time-series and explainable machine-learning models to forecast revenues, expenses, balance sheet items and other financial variables, incorporating structural breaks and regime changes where relevant.
• Manage the end-to-end model-development lifecycle, including data preparation, feature engineering, model selection, validation, testing, uncertainty quantification, documentation, deployment and ongoing monitoring.
• Build scalable analytical tools, production-grade AWS Cloud infrastructure, and AI and agentic capabilities to support scenario development, calibration, implementation, analysis and reporting.
• Partner with business divisions, Finance, Risk and other corporate functions to translate planning and risk-management requirements into quantitative methodologies and decision-ready analysis.
• Prepare clear, auditable materials supporting internal governance, Model Risk Management review, regulatory requirements and remediation activities.

Minimum Education and Experience Requirements

Candidates must meet one of the following requirements:

• PhD degree, or foreign equivalent, in Statistics, Economics, Econometrics, Computer Science, Applied Mathematics, Physics, Financial Engineering or a related quantitative field. No prior professional work experience is required.
• Masters degree, or foreign equivalent, in one of the above or a related quantitative field, plus one year of experience in the job offered or a related quantitative modeling, scenario-design, financial-engineering or quantitative-development role.
• Bachelors degree, or foreign equivalent, in one of the above or a related quantitative field, plus three years of experience in the job offered or a related quantitative modeling, scenario-design, financial-engineering or quantitative-development role.

Preferred profile: PhD graduates with strong academic research backgrounds. For non-PhD candidates, publications, open-source contributions or substantial quantitative projects may demonstrate exceptional technical and research capability.

Special Skills Required to Perform the Job

Relevant experience may be gained through graduate research, coursework, dissertation work, internships or professional employment. Required experience is zero years with a PhD, one year with a Masters or three years with a Bachelors, and must include:

• Programming: Rust, Python or C++ for quantitative modeling, data analysis, scientific computing, model implementation or agentic workflows.
• Scenario Design, Econometrics and Financial Modeling: Development, calibration, testing and documentation of macroeconomic and financial scenarios and models, including forecasting revenues, expenses, balance sheet items and other economic and financial variables using time-series, structural-break and regime-switching techniques.
• Machine Learning, Model Development and Uncertainty Quantification: Feature engineering, variable and model selection, explainable machine learning, causal and non-parametric methods, hyperparameter tuning, validation, performance testing, Monte Carlo simulation, conformal prediction, monitoring and technical documentation.
• Data, Cloud and AI Engineering: Processing large structured and unstructured datasets using SQL and database technologies, and implementing scalable quantitative, machine-learning and agentic solutions using AWS, etc.
• Communication and Collaboration: Translation of business, planning, scenario-design and risk-management requirements into quantitative methods, technical specifications and decision-ready senior-management reporting.