Credit Model Development & Data Science Analytics

Job title : Credit Model Development & Data Science Analytics

Duration: 6 months; with possible extensions

Location : Any US city; Prefer EST/CST working hours.

Pay Range: $40.54/hr. - $50/hr.

MUST HAVE: Detailed-oriented, high level of intellectual curiosity and strong sense of ownership. Experience in developing statistical loss forecasting models, PD/ EAD modeling desired

Essential Responsibilities:
• Develop, implement, and maintain an integrated loss forecasting and capital modelling suite that supports overall alignment between baseline and stressed scenarios, as well as capital planning initiatives using PySpark/Python/SAS or other programming language and big data
• Support the development of balance and revenue forecasting models, encompassing data, statistics, modeling and business acumen
• Extract and analyze client level data using structured and/or unstructured data across several data warehouses to generate actionable insights and inputs to model development
• Analyze data to identify patterns and trends across sales/payment/delinquency behaviour.
• Adapt automation and develop alternative predictive methodologies (Machine Learning) and/or cloud initiatives (AWS) to current and future models to enhance functionality
• Plan and execute self-driven analytics using next generation technologies, prepare analysis and reports to support discussions on key analytics and model aspects to drive decision making
• Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
• Develop attribution analysis and synthesize results to evaluate the applicability of existing models for cross-functional use, identify gaps and develop solutions to reduce process redundancies
• Familiarity with Model Governance trends/developments across the banking sector, especially as related to credit card or consumer lending (SR11-7)
• Strong communication skills to facilitate complex discussions in productive and collaborative manner
• Develop alternative predictive methodologies/ tools to better identify credit dynamics in portfolio performance

Qualifications/Requirements:
• Bachelors or Masters in Mathematics/Statistics, Computer Science, Economics, Finance or other quantitative discipline; or in lieu of a degree 5+ years’ experience in Risk, Finance, Consumer Lending
• 3+ years of experience in Consumer Lending statistical modeling/analytics, preferably related to CECL and/or Loss Forecasting modeling for credit cards
• 2+ years in coding with Python, PySpark or other equivalent language within the past 5 years
• Detailed-oriented, high level of intellectual curiosity and strong sense of ownership
• Good business acumen and the ability to connect data with business decisions
• Experience in developing statistical loss forecasting models, PD/ EAD modeling desired