Risk Data Scientist

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Job Description:

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 and mentors junior risk data scientists as well as independently contributes to solving complex business problems and enabling effective risk management. Additionally, the position at this level requires effective leadership and project management skills as well as in-depth knowledge in quantitative analytical methods, data management, visualization, and programming skills suitable to drive data-driven decisions.

Primary Responsibilities Mentors and guides junior risk data scientists

Plans and manages multiple concurrent projects

Carefully leverages data science, including advanced quantitative solutions, models, tools, and analysis to evaluate and provide solutions to complex business problems and effectively manage a wide spectrum of risks to the company

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

Conducts research to evaluate the ongoing appropriateness of existing or proposed quantitative solutions

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 leaders and other constituencies, including external auditors and prudential regulators

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

Works on all phases of model lifecycle, including research, data collection, model development and variable selection, model implementation and testing, model documentation, and ongoing monitoring of model to ensure it is working as intended; shepherds the model through the model validation process

This position is exempt from timekeeping requirements under the Fair Labor Standards Act and is not eligible for overtime pay.

Requirements Bachelor's degree and six (6) years of related experience in a quantitative/analytical/STEM field

Or Master's degree and four (4) years of related experience in a quantitative/analytical/STEM…