Credit Review Data Scientist Senior

Serves as the primary technical resource responsible for data extraction, transformation, analysis, dashboard development, and operational reporting supporting Credit Review activities. Serves as a "hands-on keyboard" analytics expert in developing complex methods, processes, and systems to consolidate and analyze diverse big data sources to generate insights and solutions for the Credit Review team, and for product enhancements. Works closely with the Credit Review Data Steward to support the Credit Review Department’s portfolio Risk Assessment process, review universe development, Reviewable Entity definition, sample selection, portfolio monitoring, and executive reporting. Assists other members of the Credit Review Professional Practices team with data visualization initiatives.

None

• Extract data from Commercial and Consumer loan systems and from data warehouse environments.
• Create repeatable Extract, Transform, Load (ETL) processes.
• Build automated datasets.

• Produce portfolio segment analyses.
• Monitor asset quality trends.
• Generate concentrations and risk stratifications.
• Support Credit Review’s Risk Assessment processes.

• Assist in determining, developing and stratifying portfolio populations.
• Generate statistically valid samples.
• Document sample methodologies.

Build and maintain dashboards tracking factors such as Credit Review’s review universe, planned versus completed reviews, testing deviation trends, delinquencies, Criticized assets, charge-offs, risk rating migration, and emerging portfolio risks.

• Support Credit Review’s Targeted Review activity.
• Perform targeted portfolio analyses.
• Respond to management requests.

• Support regulatory examinations.

Complete other related duties as assigned.

N/A

• Bachelor’s degree and a minimum of 5 years related experience, or in lieu of a degree, a combined minimum of 9 years higher education and/ or work experience, including a minimum of 5 years related experience
• Experience working with multiple statistics and data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias
• Proficiency with pertinent statistical software and languages and tools
• Experience with various hybrid databases both on premise and in the cloud
• Intermediate level knowledge of Structured Query Language (SQL) and Not Only Structured Query Language (nSQL)
• Intermediate understanding of modeling techniques such as Bayesian Modeling, Classification models, Cluster analysis, Neural Network, Non-parametric methods, and Multivariate statistics
• Experience analyzing large data sets

• Credit Risk Management or Credit Review experience
• Extensive knowledge of and familiarity with data management, analysis, and warehousing products, systems and workflows (Python, Power BI, Alteryx, etc.)
• Highly proficient with Data Governance Tools and with data warehousing concepts
• Experience participating in the remediation of complex data issues
• Strong understanding of statistical sampling methodologies and statistical analysis techniques and tools
• Strong portfolio analytics capabilities
• Strong data visualization skills (Power BI, Tableau)
• Understanding of AI, Generative AI, and Machine Learning applications and capabilities are considered a plus
• Prompt engineering skills are considered a plus