Risk Data Scientist

Regions Bank•Texarkana, TX

4d•$101,008 - $145,720•Onsite

About The Position

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.

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 field Or Ph.D. and four (4) years of related experience in a quantitative/analytical/STEM field
Two (2) years of hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka
Three (3) years of working experience with statistical and predictive modeling concepts and approaches such as machine learning, clustering and classification techniques, and artificial intelligence
Three (3) 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
Advanced Structure Query Language (SQL) skills
Comfortable with both relational databases and Hadoop-based data mining frameworks
Deep understanding of statistical and predictive modeling concepts, machine learning approaches, clustering and classification techniques, and recommendation and optimization algorithms
Expertise in analyzing large, complex, multi-dimensional datasets
Proficient in visualization tools like Power Business Intelligencer (BI) and Tableau
Strong business acumen with the ability to communicate with both business and Information Technology (IT) leaders
Strong leadership and project management skills

Nice To Haves

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
Experience with CI/CD environments (Snowflake, Harness, Artifactory, etc.) preferred
Ability to orchestrate large data pipelines efficiently (Spark, Pyspark, Snowpark)

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