AG Quant Labs is a quantitative research and financial analytics startup focused on building institutional-grade modeling systems in an increasingly complex regulatory and economic landscape. We specialize in advanced forecasting frameworks, dynamic risk modeling, and data-driven decision architecture for financial institutions and advisory platforms.

We are seeking a technically rigorous Quantitative Analyst with deep experience in credit risk modeling, regulatory capital analytics, and forward-looking loss forecasting frameworks including CECL and CCAR.

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Role Overview

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As a Quantitative Analyst at AG Quant Labs, you will design, develop, and validate advanced credit risk and stress testing models. This role requires strong mathematical foundations, regulatory modeling expertise, and the ability to translate complex macroeconomic scenarios into robust, production-ready forecasting systems.

You will work directly on:

• CECL lifetime loss estimation models

• CCAR/DFAST stress testing frameworks

• Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) modeling

• Macroeconomic scenario design and variable selection

• Portfolio-level loss forecasting and capital impact analytics

• Model validation, sensitivity analysis, and performance backtesting

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Key Responsibilities

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• Develop forward-looking credit loss models under CECL using econometric and machine learning techniques

• Build and maintain CCAR stress testing models incorporating macroeconomic variables and scenario projections

• Construct lifetime expected loss models with prepayment, default, and recovery assumptions

• Design and implement portfolio segmentation and risk factor modeling strategies

• Perform challenger model development and benchmarking

• Conduct model documentation, validation support, and regulatory readiness analysis

• Translate regulatory guidance into robust quantitative methodologies

• Automate model pipelines using Python and SQL

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Required Qualifications

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• Master’s or PhD in Quantitative Finance, Financial Engineering, Statistics, Mathematics, Economics, or related STEM discipline

• Strong expertise in CECL modeling frameworks and lifetime loss estimation methodologies

• Direct experience with CCAR, DFAST, or regulatory stress testing environments

• Advanced knowledge of econometrics, time-series analysis, and regression modeling

• Proficiency in Python (pandas, NumPy, SciPy, scikit-learn), SQL, and advanced Excel

• Deep understanding of PD/LGD/EAD modeling and credit portfolio analytics

• Experience incorporating macroeconomic variables into forecasting systems

• Ability to write technical documentation suitable for audit and regulatory review

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Preferred Qualifications

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• Experience working within regulated financial institutions

• Familiarity with model risk management frameworks (SR 11-7)

• Knowledge of capital adequacy modeling and RWA calculations

• Experience deploying models in cloud or scalable production environments

• Background in scenario analysis and sensitivity testing

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Location: Dallas, TX or Remote

Employment Type: Permanent, Full Time

 

Why Join AG Quant Labs

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• Build next-generation regulatory and risk modeling systems from first principles

• Work in a highly technical, startup environment with significant ownership

• Direct exposure to institutional-grade modeling challenges

• Opportunity to influence quantitative infrastructure and research direction

• Competitive compensation aligned with expertise

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If you are driven by technical precision, regulatory modeling depth, and advanced quantitative problem-solving, we invite you to apply using the form above and help shape the future of risk analytics at AG Quant Labs.

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