Data Scientist-2

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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 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.

Primary Responsibilities

• 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
• Requirements
  • Bachelor’s degree and six (6) years of related experience
  • Or Master’s degree and four (4) 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 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 Intelligence (BI) and Tableau
  • Strong business acumen with the ability to communicate with both business and Information Technology (IT) leaders
  • Strong communication skills through data visualizations as well as written and oral presentations

  Additional Responsibilities

  • Design and develop transaction monitoring scenarios.
  • Improves segmentation for the scenarios/models using techniques such as clustering.
  • Executes periodic tuning for the threshold parameters of the scenarios through sample collection.
  • Develops post processing models to reduce the number of false positives generated by the scenarios using techniques such as rare event logistic regression and machine learning algorithms.
  • Collect and retrieve information associated with customers from the web, news articles, or other publications via Natural Language Processing techniques and/or Large Language Models.
  • Researches and develops algorithms that incorporates fuzzy logic for OFAC sanction screening and other types of screening processes.
  • Implementation of models.
  • Develops and executes ongoing monitoring plan to monitor the performance of the models.
  • Documentation of the model development, especially the implementation process.
  • Supports model validation activities.
  • Ad hoc analysis to address requests from business partners.

  Additional Skills/Experiences

  • High proficiency with Python, R, SAS, and SQL.
  • Advanced data sourcing and management skills.
  • Enhanced experience with web scraping.
  • Knowledge and experience with CDSW platform.
  • Experience with software development, especially deployment.
  • Experience with classification models.
    

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