This position will act as the subject matter expert (SME) in Credit Bureau data, Consumer Credit Reports, and the different versions of Credit Scores.

Familiar with raw credit in-files and data reported to Credit Bureau, and how these data feed into credit scoring.

Lead modeling and analytical underwriting and credit risk projects using techniques from statistics, machine learning and deep learning.

Conduct analytics and mentor junior staff in obtaining quantitative insight into mortgage underwriting issues and questions.

Develop innovative approaches to applying mortgage, credit bureau, customer permission data and other third-party data for credit risk evaluation.

Provide modeling and analytical support to a line of business or product area, functioning as day-to-day technical specialist.

Create and present materials to effectively communicate quantitative findings.

Write documentation to explain analytical decisions and follow model oversight.

Make efficient use of the latest computing technologies to take advantage of big data and efficiently conduct analyses.

Perform exploratory analysis and research and present results to both technical and non-technical audiences.

Visualize data and analytical results to communicate complex ideas.

Doctorate degree (or Master's degree with equivalent work experience) in statistics, data science or a related quantitative field.

5+ years of relevant experience applying predictive modeling techniques or data analytics to large datasets is preferred.

3+ years of relevant experience on leading model development, model review or fair lending projects.

Experience applying predictive modeling techniques from data science, statistics, machine learning, and econometrics to large data sets. Qualifying coursework may include—but is not limited to—data science, statistics, machine learning, optimization, numerical analysis, scientific programming, computational methods, supervised learning, unsupervised learning, text mining, and image analysis.

Experience writing computer programs to implement data science pipelines and predictive algorithms. Programming languages may include—but are not limited to—Python, R, SQL, and SAS.

Experience using and manipulating large structured databases.

Experience with mortgage industry data in general and consumer credit bureau data specifically from Experian, Equifax, and Trans Union is desired.

Exceptional quantitative and analytics skills

Strong knowledge of statistical models, tools and techniques

Strong programming skills

Effective interpersonal skills including the ability to collaborate successfully with other team members.

Strong oral and written communication skills with ability to succinctly describe complex technical scenarios in simple, easily-understood terms.