Sr. Data Scientist

Cortex • Irving, TX

13d • Remote

About The Position

Develop, maintain, and validate statistical and AI/ML models that assist in optimizing credit and operational strategies across the consumer lifecycle. Consumer acquisition models across various channels including Direct mail, lead generation and other strategic partnerships. These models are employed to predict with accuracy, metrics such as propensity to response, take up rates and Customer Acquisition Cost (CAC). Underwriting models that assist in estimating early pay default, credit losses, and Loss Given Default (LGD) metrics. Fraud detection algorithms that identify fraud rings, third party fraud and first party fraud in a variety of customer acquisition scenarios. Development of account management models that predict likelihood of line usage, propensity to pay and default metrics. Collaborate with the portfolio management, marketing, loss mitigation and default services teams to deploy statistical and AI/ML algorithms that improve consumer experience, precision in estimating credit worthiness and improvement in unit economics across the consumer life cycle. Collaborate with the technology, product and other teams to deploy the algorithms into decision engine or other platforms in an efficient manner. Participate and lead data studies with external parties including Transunion, Experian, and Equifax to improve the efficiency of the algorithms used across the consumer lifecycle. Undertake R&D and testing of state-of-the-art modeling techniques including neural networks, deep learning, unsupervised techniques and benchmark against traditional and statistical modeling algorithms such as logistic regression, linear regression, principal component analysis etc. Requires a Master's degree in Statistics, Mathematics, or a related field (or its equivalent) plus 4 years of related experience. Requires 4 years of experience with the following: Statistical and AI/ML model development for credit and operational strategies; Machine learning techniques and algorithms (including neural networks, deep learning, unsupervised learning); Risk strategy implementation for credit line management, collections, and authorizations; Statistical software (SAS), programming language (Python and R), and query languages (SQL) for data analysis. Telecommuting permitted. Applicants who are interested in this position may apply https://www.jobpostingtoday.com/ Ref # 10704.

Requirements

• Master's degree in Statistics, Mathematics, or a related field (or its equivalent)
• 4 years of related experience
• 4 years of experience with Statistical and AI/ML model development for credit and operational strategies
• Experience with Machine learning techniques and algorithms (including neural networks, deep learning, unsupervised learning)
• Experience with Risk strategy implementation for credit line management, collections, and authorizations
• Experience with Statistical software (SAS), programming language (Python and R), and query languages (SQL) for data analysis

Responsibilities

• Develop, maintain, and validate statistical and AI/ML models that assist in optimizing credit and operational strategies across the consumer lifecycle.
• Collaborate with the portfolio management, marketing, loss mitigation and default services teams to deploy statistical and AI/ML algorithms that improve consumer experience, precision in estimating credit worthiness and improvement in unit economics across the consumer life cycle.
• Collaborate with the technology, product and other teams to deploy the algorithms into decision engine or other platforms in an efficient manner.
• Participate and lead data studies with external parties including Transunion, Experian, and Equifax to improve the efficiency of the algorithms used across the consumer lifecycle.
• Undertake R&D and testing of state-of-the-art modeling techniques including neural networks, deep learning, unsupervised techniques and benchmark against traditional and statistical modeling algorithms such as logistic regression, linear regression, principal component analysis etc.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level