Data Analytics

Fort Worth, TX

$98 - 125K Range (Depending on experience)

Also an 8% Bonus (performance based)

3-5 years experience preferred in the analytical or financial services field.

Technical skills / abilities:

Strong technical aptitude. Familiarity with ETL processes, SQL, SAS, R highly preferred.

Leverage analytical tools to perform routine data collection, analysis and reporting of credit risk KPI’s (i.e., delinquencies, vintage losses rates, application conversion) to monitor and communicate trends of BorrowWorks’ loan portfolios.

Key Responsibility 1: Mine, model, analyze large datasets, and utilizes predictive modeling techniques with an emphasis on optimizing credit risk and analyzing customer behavior using the following predictive modeling techniques: linear/logistic regression, factor analysis, decision trees, clustering, segmentation, etc.

Key Responsibility 2: Build and validate a variety of statistical models, provides analytic support, and develops new criteria and/or strategies.

Key Responsibility 3: Perform product or other segment level analytics to analyze performance of existing products and assist with roll-out of new product policy and pricing strategies for various product offerings.

Key Responsibility 4: Implement credit models, rules, scorecards, and strategies into the company’s Decision Engine software that houses the Credit Policy. Work with internal IT resources to test and deploy new implementations.

Key Responsibility 5: Collaborate with other departments across the organization to drive improvement in quality, volume, service, and profitability.

Key Responsibility 6: Identify process improvement opportunities and involve in developing/executing projects

Education:

Bachelors degree in a quantitative subject. (Mathematics, Statistics, Economics preferred)

Required experience:

3-5 years experience preferred in the analytical or financial services field.

Technical skills / abilities:

Strong technical aptitude. Familiarity with ETL processes, SQL, SAS, R highly preferred.