Associate, Quantitative Strategist, Core Planning and Analysis Strats

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.