Truist Bank is seeking a Data Scientist II specializing in fraud strategy to perform advanced analytics and provide actionable insights to improve business outcomes. The ideal candidate will have a strong background in quantitative fields, statistical methods, and data science technologies while being capable of delivering data-driven solutions to various stakeholders
Job Summary
Perform sophisticated analytics (statistical and predictive analytics, machine learning modeling, etc.) to provide actionable insights that improve business outcomes and minimize risk and also provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies around analytics.
Independently perform sophisticated data analytics (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
Exercise sound judgment and foster risk management culture throughout design, development, and deployment practices; partner with cross-functional teams to coordinate rules on data usage, data governance and analytics capabilities.
Matching Summary
Match Score: 85
Truist Bank is seeking a Data Scientist II specializing in fraud strategy to perform advanced analytics and provide actionable insights to improve business outcomes. The ideal candidate will have a strong background in quantitative fields, statistical methods, and data science technologies while being capable of delivering data-driven solutions to various stakeholders.
Skills & Requirements
Must-have
statistical and predictive analytics
machine learning modeling
data visualizations
custom code deployment
SQL data extraction
Hadoop, Pig, Hive, or NoSQL, Spark
Nice-to-have
natural language processing
emerging methods and technologies
risk management culture
Key Requirements
Bachelor’s degree and four or more years of experience in a quantitative field
Understanding of statistical methods
Familiarity with linear algebra concepts
Understanding of data cleansing and preparation methodologies