Senior Lead Mortgage Data Analytics Specialist
On-siteCharlotte, North Carolina, United States
Job Summary
Lead the Mortgage Model Development Center's mortgage data analytics capability, defining strategy, roadmap, and delivery outcomes across MBS, EMBS, loan performance, and Intex datasets. Build scalable Python-based frameworks for data ingestion, transformation, quality control, and feature engineering while establishing rigorous standards for documentation and production workflows. Partner with model development, validation, and business stakeholders to translate loan-level data into actionable insights, driving adoption of AI-enabled workflows for anomaly detection and analytics acceleration. This role requires on-site presence in Charlotte, NC, with up to 10% travel, reporting to the Investment Portfolio group which manages the bank's AFS and HTM securities.
Required Qualifications
- 7+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Deep experience with mortgage data, including MBS/EMBS, loan-level performance, agency datasets, Intex, prepayment, delinquency, default, and loss analytics
- Ability to travel up to 10% of the time
- Must be able to work on-site
Desired Qualifications
- Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science
- Strong hands-on Python and SQL experience, including pandas, NumPy, PySpark, or comparable large-scale data analytics tools
- Demonstrated experience owning or leading a data analytics function, including strategy, operating model, governance, and delivery accountability
- Experience building reusable analytics tools, data pipelines, quality controls, dashboards, or production-grade analytical workflows
- Ability to communicate complex mortgage data concepts clearly to senior stakeholders, modelers, validators, technologists, and business partners
- Experience applying AI, machine learning, or automation techniques to financial data analytics, data quality, or unstructured data extraction
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