Quantitative Analyst

AmeriSave Mortgage Corp.

5d•$80,000 - $150,000•Remote

About The Position

AmeriSave Mortgage has set the standard in online mortgage lending with over $130 billion in funded loan volume. As one of the top-rated, largest privately-owned online mortgage lenders in the nation, our mission is to deliver beneficial, responsible home lending solutions with unwavering integrity, dedication and excellence. We leverage cutting-edge technology and data-driven strategies to provide exceptional service to our customers. Our employees are the driving force behind our success. We believe in the power of a dynamic and talented workforce and creating an environment where your contributions are not just recognized, they’re celebrated. Your success is our success, and we are seeking skilled professionals who are ready to bring their A-game, exceed benchmarks and enhance the overall excellence of AmeriSave, while also growing and advancing their careers. At AmeriSave, we're one team with one shared dream - to be the best. Let’s redefine excellence together! What We’re Looking For: We are seeking a Quantitative Analyst to join the Enterprise Intelligence team. This fully remote role will focus on developing and maintaining sophisticated quantitative models and conducting deep analysis of business metrics to uncover deficiencies and opportunities that drive revenue and profit. This is a fast-paced, high-ownership environment where ideas move quickly from concept to production. You will work on projects that are not only technically challenging but also highly visible and business-critical, delivering tangible impact across the organization. Analysts are empowered to take initiative, think creatively, and solve hard problems—resulting in deeply rewarding and high-leverage contributions that drive revenue and profit for the company.

Requirements

Quantitative Modeling Expertise: 3+ years of experience developing statistical, econometric, or machine learning models in a business environment.
Advanced SQL Skills: Advanced T-SQL queries, stored procedures, and data transformations in Microsoft SQL Server.
Python Proficiency: Strong experience with Python for data analysis, statistical modeling, and process automation.
Analytical Problem Solving: Ability to independently translate complex business problems into quantitative solutions. Skilled at designing experiments, validating hypotheses, and communicating findings clearly.
Business Acumen: Experience evaluating business metrics and KPIs to identify revenue and profit improvement opportunities, operational inefficiencies, and performance trends.
High-speed internet required for remote work, Cable or Fiber ONLY with the ability to connect via Ethernet. Minimum speeds: 70/30 Mbps (basic), 200-300/35-70 Mbps (shared), 500-1,000/100+ Mbps (heavy use).

Nice To Haves

College degree preferred

Responsibilities

Design and Develop Quantitative Models: Build advanced statistical and quantitative models to support forecasting, pricing optimization, and risk assessment. Ensure models are efficient, transparent, and suitable for deployment in production environments.
Analyze Business Metrics & Identify Opportunities: Rigorously analyze operational and financial metrics to detect process deficiencies, performance gaps, and new opportunities to improve revenue and profitability. Develop actionable recommendations and partner with business leaders to prioritize and implement improvements.
Data-Driven Performance Measurement: Create and maintain frameworks for tracking and analyzing business performance across the mortgage lifecycle. Develop metrics, dashboards, and automated reports that enable stakeholders to monitor progress and act on insights.
SQL-Based Analytics Solutions: Author and optimize complex T-SQL code to extract, transform, and analyze large volumes of production data. Develop stored procedures and workflows that operationalize model outputs and support business monitoring.
Python Modeling and Automation: Write Python scripts to perform statistical analysis, automate data pipelines, and integrate machine learning workflows. Leverage Python for data preparation, model training, and result generation.
Model Validation and Monitoring: Rigorously test, validate, and monitor model performance over time. Develop processes to recalibrate or enhance models as needed to maintain accuracy and business alignment.