Financial Modeling & Analytics Quantitative Analyst

Position Title

Financial Modeling & Analytics Quantitative Analyst

New York, NY 10018

The Quantitative Analyst is a key member of the Financial Modeling and Analytics team, advancing Flagstar’s understanding of its customers, balance sheet, and risk exposures. This Analyst uses advanced quantitative and technical skills to produce insightful reporting, modeling analysis, and recommendations that enable sound and profitable risk management and strategic decisions. The Analyst may have assignments related to credit risk management for current expected credit loss ("CECL"), macroeconomic scenarios ("Stress Testing"), asset and liability management ("ALM"), allowance for loan and lease losses ("ALLL") models and analytics.

This Analyst is responsible for maintaining the skills and knowledge relevant to their assigned job responsibilities. In addition, the analyst promotes and advocates the adoption of appropriate new methodologies for quantitative models and analysis that benefit the Bank, and provides assistance with identifying and designing quantitative solutions to problems as presented by management to further the Bank’s strategic plans.

Authorization to work in the US with no required sponsorship.

$62,910.75‑$ Pay Range:
Local Minimum Wage - $0.00 - $0.00

• Development and maintenance of quantitative financial models used in risk measurement, income simulations, balance sheet management, or asset valuations, including complex financial, econometric, and statistical frameworks. Implements, tests and documents internally and externally developed and/or licensed models. Assists with the integration of models within the FMA team and among other teams to ensure consistency and accuracy across all model outputs.
• Analysis and documentation of model output, ongoing performance monitoring, synthesis of information, and communication of conclusions and recommendations in a clear and concise manner to senior managers and executives.
• Testing of established models, including back‑testing, benchmarking, sensitivity testing, and scenario analysis.
• Utilize industry‑standard quantitative software, data science programming languages (Python), database systems (SQL), and workstation productivity applications (Tableau) to produce required deliverables.
• Assist with the analysis and documentation of Management, Internal Audit, Enterprise Risk Management and Regulatory findings and requirements.
• Develop automated solutions to simplify challenging and repetitive processes.

• Perform special projects, and additional duties and responsibilities as required.
• Consistently adhere to regulatory and compliance policies and standards linked to the job as listed and complete required compliance trainings. Accountable to maintain compliance with applicable federal, state and local laws and regulations.

• Education level required:
  Undergraduate Degree (4 years or equivalent) in Finance, Mathematics, Economics, Engineering, or similar.
• Minimum experience required: 1+ years of experience with credit risk modeling, including linear modeling frameworks (Ordinary Least Squares, Logistic Regression, Generalized Linear Models, Credit Risk Scorecards, Time Series, Roll Rate, Transition Matrix, etc.).

• Education level preferred:
  Master’s Degree (or Postgraduate equivalent) in Finance, Mathematics, Economics, Engineering, or similar.
• Experience with quantitative fixed‑income, programming and financial modeling. Direct experience working with and analyzing model data preparation requirements (quality control, transformations, re‑classifications, etc.).
• Experience with CCAR/DFAST modeling requirements.
• Experience with ALLL/CECL modeling requirements.
• Experience deploying credit loss models into a production ALM environment.
• Experience using third‑party macroeconomic forecast data.

• Knowledge of finance concepts with a solid understanding of statistics and quantitative methods.
• SAS: experience developing and documenting well‑structured, standards‑based SAS programs using Enterprise Guide, SAS data analysis…