Associate

About the Role

As an Associate Quantitative Strategist (Strat) within the Core Planning and Analysis Strats team, you will focus on:

• Designing, developing, and implementing quantitative models for Budget Planning & Management, including modeling and forecasting revenues, expenses, and balance sheet dynamics.
• Designing and engineering AI agents to automate analysis, reporting, and decision support across the planning lifecycle.
• Building and deploying scalable solutions in the Cloud, primarily in Python, with opportunities to contribute to the adoption of Rust for performance-critical scientific computing.

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

Key Responsibilities

• Design, develop, implement, and document advanced quantitative models and scenarios for time-series forecasting of revenues, expenses, and balance sheet items. Incorporate economic, financial, and business variables, 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.
• Collaborate with cross-functional stakeholders across business divisions, Finance, Risk, and other corporate departments. Translate user needs into model specifications, analytical metrics, dashboards, and reports.
• Execute the end-to-end model development lifecycle, including 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 for analytical, data science, and reporting capabilities through interactive and batch interfaces. Manage agent orchestration, context management, knowledge base integration, tool calling, and AI lifecycle management.
• Conduct simulation studies, provide theoretical justifications, and perform model performance testing.
• Create and maintain technical documentation for Model Risk Management (MRM) reviews, facilitate finding remediation, and ensure ongoing model monitoring.

Requirements & Tech Stack

• Programming Languages:
• Strong proficiency in Python.
• Experience with or interest in developing in Rust (or C++) for performance-critical numerical code is a plus.
• 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.
• 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.

Qualifications

• Required Field of Study: Statistics, Computer Science, Applied Mathematics, Physics, or a related quantitative field.
• Education Level:
• PhD graduates with strong academic research backgrounds are highly preferred.
• Consideration will also be given to experienced Masters and Bachelors degree holders.
• Experience: Prior experience—satisfied through professional work or, for PhD candidates, graduate-level research, coursework, or dissertation work—must demonstrate the required skills.
• Evidence of exceptional ability through contributions to open source projects, publications, or other work and activities is valued.