Risk Data Scientist

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At Regions, the Risk Data Scientist researches, models, implements, and validates algorithms (predictive and prescriptive) to analyze diverse sources of data to achieve targeted outcomes. The position at this level works with multiple teams of data scientists, analysts, and visualization experts contributing independently to solve business problems with high complexity and enable effective risk management. Additionally, the position at this level requires in-depth knowledge in quantitative analytical methods, data management, visualization, and programming skills suitable to drive data-driven decisions.

Works with large, structured, and un-structured datasets Uses quantitative and analytical techniques to accelerate profitable growth and monitor and mitigate risk - unlocking value across all functional areas of business Uses Big Data tools (e.g. Hadoop, Spark, H2O, CDSW, Domino Labs, etc.) to build data analytics solutions Builds machine learning and Artificial Intelligence (AI) models from development through testing and validation Designs rich data visualizations to communicate complex ideas to business leaders and executives Communicates outcomes and proposed business solutions to senior Risk Data Scientists Draws insights from data to make quick, well informed decisions with available information Demonstrates ability to continuously learn and provide value in a dynamic environment Understands all phases of the model lifecycle, ensuring that models and associated documentation comply with model validation expectations This position is exempt from timekeeping requirements under the Fair Labor Standards Act and is not eligible for overtime pay.

Bachelor's degree and four (4) years of related experience Or Master's degree and two (2) years of related experience Or Ph.D. and two (2) years of related experience in a quantitative/analytical/STEM field One (1) year of hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka Two (2) years of working experience with statistical and predictive modeling concepts and approaches such as machine learning, clustering and classification techniques, and artificial intelligence Two (2) years of working programming experience analyzing large, complex, and multi-dimensional datasets using a variety of tools such as SAS, Python, Ruby, R, Matlab, Scala, or Java Preferences Background in banking and/or other financial services Experience in Agile Software Development May require experience in libraries such as TensorFlow, Pytorch, or Keras Knowledge in Google Analytics and/or Adobe Digital Skills and Competencies Advanced Structured Query Language (SQL) skills Comfortable with both relational databases and Hadoop-based data mining frameworks Deep understanding of statistical and predictive modeling concepts, machine