Drive actionable insights and scalable reporting across the mortgage lifecycle—from acquisition to servicing. Build predictive and analytical models that support strategic decision-making.
Requirements
- Experience setting requirements and influencing the development of models for fraud detection, CLV estimation, or risk scoring; experience with compliance automation or underwriting is a plus.
- Familiarity with modern ML Ops practices (data/model versioning, CI/CD, performance monitoring, and bias detection).
- Experience with regulatory compliance, privacy, and auditability in model development (e.g., ECOA, RESPA, CFPB regulations).
- Advanced proficiency in SQL and Excel, experience with any of the modern visualization tools (e.g., Tableau, Power BI); ability to quickly learn basic Python/SAS skills.
Responsibilities
- Design and implement predictive models, reporting pipelines, and statistical techniques across key mortgage workflows—such as risk scoring, churn prediction, segmentation, and structured document processing where applicable—to improve metrics like Conversion Rate, Delinquency Rate, and Customer Lifetime Value (CLV).
- Translate business problems into well-scoped analytics and modeling initiatives, including executive reporting and performance dashboards.
- Drive the development of automated reporting systems and decision-support tools.
- Build robust data pipelines and dashboards in collaboration with data engineering, ensuring scalability, monitoring, and model governance in production environments.
- Ensure compliance with data privacy regulations (e.g., CCPA) and model auditability, particularly in financial and regulatory-sensitive applications.
Other
- Minimum 10 (or 7, post-PhD )years of experience required, ideally in mortgage, fintech, or financial services
- Strong analytical and business decision making skills, with experience influencing top-level leaders in high-stakes strategic decisions.
- Demonstrated ability to translate business objectives into technical modeling goals and measurable success metrics.
- Excellent communication skills for both technical and non-technical stakeholders, including ability to present complex results with clarity.
- Must pass the Company’s Background Screening process prior to beginning employment. Additionally, as a condition of employment, you may be required to pass client-specific background check requirements or Federal/State licensing requirements, if applicable.
