Truist Strategic Risk Data Scientist II 

United States, North Carolina, Charlotte 

413589694

Regular or Temporary:

English (Required)

1st shift (United States of America)
Please review the following job description:

• Perform sophisticated analytics (statistical and predictive analytics, machine learning modeling, etc) to provide actionable insights that improve business outcomes and minimize risk and to provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies throughout the entire lending product credit life cycle.
• Independently performs various analytic techniques (encompassing data mining, inferential statistical analysis, and predictive analytics, for example) to support the entire credit life cycle of the lending product.
• Leveraging strong foundation of business and industry knowledge, identifies actionable insights from various (or multiple) sources of data that measurably improve business outcomes or reduce business risk.
• Collects and prepares data for analysis, performs exploratory to advanced predictive and/or modeling analytics, and identifies data relationships (patterns and trends).
• Provides consultation to business leaders and other stakeholders on how to leverage analytic insights to build actionable strategies. Crafting effective presentations and delivering results to multiple levels of management.
• Provide analytic recommendations for credit policy development, credit initiatives (underwriting, new products, new populations, new channels, portfolio strategies, line management, pricing, etc.), and monitor initiatives to ensure that performance is within established risk appetite.
• Utilize internal and external data to drive decision-making and identify opportunities. Monitor trends in the industry. Seeks out new sources of information for benchmarking and strategy development.
• Focus on getting the most out of data and exploring new areas of analysis.

Requirements

• Must have a Bachelor’s degree in Statistics, IS, Data Science, Analytics or related quantitative field.
• Must have 4 years of experience in quantitative or data analytics positions performing/utilizing the following:

• Credit risk management, risk strategy, and/or risk analytics across diverse high volume lending businesses or equivalent experience
• Statistical methods, including a broad understanding of classical statistics, prediction, classification, clustering, econometrics, time-series, and primary statistical tests using various packages in Python and SAS
• Data cleansing and preparation methodologies, including regex, filtering, indexing, interpolation, and outlier treatment.
• Natural Language Processing techniques and other related Deep Learning knowledge.
• Developing interactive story telling scorecards and dashboards using stacked bars, bar graphs, scatter plots, geographical maps and other visualizations using Excel and Tableau best practices.
• Data mining, data programming and data visualization (working with large, often disparate, corporate datasets furthermore automate the process using VBA to ensure minimal manual effort.
• Extracting, structuring, interpreting and analyzing enterprise level data to identify key metrics and transform data into meaningful information; Investigating and documenting variance analyses with accuracy and data quality with respect to regulatory reporting
• Delivering ad-hoc SAS programming solutions to perform complex requests and to identify statistical abnormalities further determining the root cause.
• Distilling and translating complex data and strategies to diverse stakeholders in clear, crisp terms and preparing standard operating procedures and training material for helping with the best practices.
• Utilizing experience with: SQL, SAS (including SAS Base, Macros, Graph, SQL, and Connect), Python, Tableau, Excel, and Powerpoint.

• Position may be eligible to work remotely but is based out of and reports to Truist offices in Charlotte, NC. Must be available to travel to Charlotte, NC regularly for meetings and reviews with manager and project teams within 24-hours’ notice.