Senior Statistical Analyst

About NAF & Position Summary:

New American Funding (NAF) is a mortgage lender offering an array of loan options. Established in 2003 and headquartered in Tustin, CA, United States

Company Website: www.newamericanfunding.com

To know more about NAF India, click here - NAF India | New American Funding

Position Summary:

As an experienced Senior Statistical Analyst you are expected to do spearhead predictive modelling and financial return-on-investment (ROI) analytics for our mortgage lending operations. This role bridges quantitative data science and executive financial decision-making. You will build, calibrate, and deploy statistical and econometric models that directly optimize loan processing workflows, forecast capacity and turn-times, identify operational bottlenecks, and quantify the financial ROI of strategic operational and technology initiatives.

This role combines quantitative analytics with financial strategy to support key decisions across NAF’s mortgage operations. The individual will develop and apply statistical and econometric models to guide capacity planning, process optimization, and technology investments, translating complex findings into actionable ROI recommendations for mortgage leadership and CFO-level stakeholders.

Key Responsibilities:

Statistical Model Development & Maintenance: Formulate, test, and calibrate robust predictive and econometric models covering loan manufacturing, processing efficiencies, underwriting throughput, and pull-through rates.

Operational ROI & Cost-Benefit Modeling: Calculate and forecast the ROI, cost per funded loan, and efficiency gains driven by automation, workflow modifications, and offshore labor arbitrage.

Predictive Operational Analytics: Leverage statistical methods (e.g., survival analysis, time-series forecasting, multivariate regression) to predict cycle times, pipeline volume volatility, and staffing/capacity requirements.

Executive Decision Support & Synthesis: Translate intricate quantitative models into actionable financial insights, executive scorecards, and strategic recommendations for US mortgage leadership and CFO-level reporting.

Model Governance & Validation: Ensure rigorous back-testing, benchmarking, sensitivity analysis, and compliance with institutional model risk management frameworks.

Detailed Roles and Responsibilities

Advanced Predictive Analytics & Process Optimization

• Analyze end-to-end mortgage lifecycle data — from lead intake and application through processing, underwriting, closing, and post-closing/servicing.
• Build predictive duration/survival models to identify loan fallout patterns, time-in-status delays, and touchpoint inefficiencies.
• Develop statistical capacity-planning algorithms to align frontline operational staffing dynamically with application volume surges and interest rate shifts.
• Apply anomaly detection and multivariate hypothesis testing to evaluate underwriting quality, error rework rates, and operational compliance.Financial Modeling, Efficiency Attribution & ROI Computation
• Partner with operational leaders and finance partners to develop attribution models tracking exact dollar savings generated by process improvements.
• Construct discounted cash flow (DCF), Net Present Value (NPV), and Internal Rate of Return (IRR) models to evaluate investments in mortgage technology (e.g., automated underwriting engines, OCR document processing).
• Track operational unit economics, including cost-per-touch, marginal cost per loan, and productivity curves across onshore and offshore teams.Pipeline Forecasting & Scenario Simulation
• Construct stochastic and Monte Carlo simulation frameworks to stress-test loan pipelines under shifting macroeconomic conditions (e.g., Federal Reserve rate decisions, refinance vs. purchase shifts).Data Analytics, Tooling & Visualization
• Query large relational and cloud databases using advanced SQL to extract and curate complex mortgage transactional data.
• Build automated scripts in Python or R to run recurring model pipelines, retraining, and drift monitoring.
• Develop interactive dashboards and scenario-planning calculators in Power BI or Tableau for operational managers and senior stakeholders.

Work Experience & Statistical Model Track Record

Required Overall Experience

4 to 6 years of hands-on experience in quantitative data analytics, statistical modeling, or financial engineering within the Financial Services / BFSI sector (direct mortgage lending, retail banking, or consumer credit preferred).

Proven Experience in Statistical & Machine Learning Models

Demonstrated history of developing and deploying models within financial or operational domains:

• Regression & Econometrics
• Multivariate Linear Regression, Logistic Regression, Ridge/Lasso, and Generalized Linear Models (GLMs) for probability-of-default or pipeline pull-through estimation.
• Time-Series & Forecasting
• ARIMA/SARIMA, Exponential Smoothing, and Prophet models applied to transaction volume, application inflows, and cash flow projections.
• Classification & Supervised Learning
• Decision Trees, Random Forest, for loan status classification, lead scoring, and defect prediction.
• Survival & Hazard Modeling
• Kaplan-Meier and Cox Proportional Hazards models applied to loan cycle times, customer attrition, and stage-gate progression.
• Simulation & Optimization
• Monte Carlo simulations, linear programming, and queuing theory for workflow capacity allocation.

Core Competencies and Educational Qualifications:

• Chartered Financial Analyst (CFA) Charter holder, or CFA Level II / III Passed with demonstrable quantitative application; OR
• Financial Risk Manager (FRM) designation; OR
• Master’s / Postgraduate Degree in Statistics, Econometrics, Quantitative Finance, Applied Mathematics, Data Science, or an MBA in Finance/Business Analytics from a tier-1/tier-2 institution.
• Hands-on experience in Python
• Advance SQL
• Advanced financial modeling in Excel
• Power BI or Tableau, for executive visual storytelling – nice to have