Risk Data Scientist

Regions Bank•Atlanta, GA

21d•$101,008 - $145,720•Onsite

About The Position

At Regions, the Risk Data Scientist researches, models, implements, and validates predictive and prescriptive algorithms using diverse data sources to achieve targeted outcomes. This role works with data scientists, analysts, and visualization experts, mentors junior risk data scientists, and independently contributes to complex business problems and effective risk management. The position requires leadership and project management skills, along with in-depth knowledge of quantitative analytical methods, data management, visualization, and programming to support data-driven decisions. The role works with large structured and unstructured datasets and applies quantitative and analytical techniques to support profitable growth and monitor and mitigate risks across the business. It uses Big Data tools and develops machine learning and artificial intelligence models from development through testing and validation. The Risk Data Scientist also creates visualizations, communicates findings and proposed solutions to senior leaders and other stakeholders—including external auditors and prudential regulators—and manages all phases of the model lifecycle, including research, data collection, development, implementation, testing, documentation, monitoring, and model validation. This is a full-time position located at the Riverchase Complex North Building in Hoover, Alabama. It is exempt from Fair Labor Standards Act timekeeping requirements and is not eligible for overtime pay.

Requirements

Bachelor's degree and six years of related experience in a quantitative, analytical, or STEM field; or a master's degree and four years of related experience; or a Ph.D. and four years of related experience.
Two years of hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka.
Three years of experience with statistical and predictive modeling concepts and approaches, including machine learning, clustering and classification techniques, and artificial intelligence.
Three years of programming experience analyzing large, complex, multidimensional datasets using tools such as SAS, Python, Ruby, R, Matlab, Scala, or Java.
Advanced SQL skills.
Comfort with relational databases and Hadoop-based data mining frameworks.
Deep understanding of statistical and predictive modeling, machine learning, clustering and classification, and recommendation and optimization algorithms.
Expertise analyzing large, complex, multidimensional datasets.
Proficiency with visualization tools such as Power BI and Tableau.
Strong business acumen and ability to communicate with business and IT leaders.
Strong leadership and project management skills.
Ability to orchestrate large data pipelines efficiently using tools such as Spark, PySpark, or Snowpark.

Nice To Haves

Background in banking or other financial services.
Experience in Agile software development.
Experience with libraries such as TensorFlow, PyTorch, or Keras may be required.
Knowledge of Google Analytics and/or Adobe Digital.
Experience with CI/CD environments such as Snowflake, Harness, or Artifactory.

Responsibilities

Mentor and guide junior risk data scientists.
Plan and manage multiple concurrent projects.
Apply data science, advanced quantitative solutions, models, tools, and analysis to solve complex business problems and manage a wide spectrum of company risks.
Work with large structured and unstructured datasets.
Use quantitative and analytical techniques to accelerate profitable growth and monitor and mitigate risk across business functions.
Research the ongoing appropriateness of existing or proposed quantitative solutions.
Use Big Data tools to build data analytics solutions.
Build machine learning and artificial intelligence models from development through testing and validation.
Design data visualizations that communicate complex ideas to business leaders and executives.
Communicate outcomes and proposed business solutions to senior leaders and other constituencies, including external auditors and prudential regulators.
Draw insights from data to make timely, informed decisions.
Continuously learn and provide value in a dynamic environment.
Work across all phases of the model lifecycle, including research, data collection, model development and variable selection, implementation, testing, documentation, ongoing monitoring, and model validation.