What are the responsibilities and job description for the Sr. Data Scientist position at CORTEX?
Job Summary:
- 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, 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 such as Transunion, Experian, Equifax and other data sources 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.
Education and Experience:
- Master's degree in a highly quantitative field (Statistics, Economics, Mathematics, or other quantitatively-oriented degree) required. Doctoral Degree is a plus.
- 2-4 years of Risk Management / Modeling / Analytical experience
- Strong quantitative / statistical modeling capabilities, with a history of building Scoring Models
- Strong SAS (Statistical Analytical Software) skills, with ability to conduct extensive data research
- Experience using machine learning techniques and algorithms
- Proficiency in using query languages such as SQL
- Experience in analyzing, recommending and implementing risk strategies pertaining to credit line management, collections, authorizations and communications.
- Effective communication skills with ability to present highly complex materials to a wider and less-technical audience
- Self-motivated, team player
- Ability to work effectively on multiple simultaneous projects in a fast paced, dynamic environment