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Job Description:

At Regions, the Risk Quantitative Model Validation Analyst serves as a member of a key strategic team that is responsible for performing independent model risk oversight activities, including model identification, determination, classification, inventory management, validation, review, issue remediation testing, reporting, and related activities. The associate will test data products, including models and analytical tools, in the areas of fraud monitoring, cybersecurity, credit scoring, marketing, BSA/AML/OFAC compliance, market risk, capital markets, operational risk, finance and accounting, loan pricing, deposit pricing, loan valuation, and economic capital.

In Model Risk Management and Validation (MRMV), the Risk Quantitative Model Validation Analyst works with multiple teams of validation analysts, governance analysts, as well as automation specialists to validate highly complex quantitative models, including Artificial Intelligence (AI) and Machine Learning (ML) approaches. The ideal candidate has project management skills as well as depth of knowledge in data management, visualization, automation, quantitative modeling methods and programming skills.

Primary Responsibilities

Performs quantitative validation test work under guidance from validation manager and/or senior validation analyst and summarizes test results, conclusions, and issues in model validation report
Works with large, structured, and unstructured datasets
Uses quantitative and analytical techniques to validate models focused on accelerating profitable growth and customer engagement, unlocking value across all functional areas of business
Communicates model issues and limitations to key stakeholders and represents Regions with regulators as needed
Uses Big Data tools (Hadoop, Spark, H2O, CDSW, etc.) to validate data analytics solutions
Validates Machine Learning and Artificial Intelligence models
Demonstrates ability to continuously learn and provide value in a dynamic environment
Develops, engages, and retains fellow MRMV associates as well as foster interest in MRMV among internal Regions associates even when MRMV does not have a posting

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 experience in a quantitative/analytical/STEM field
OR Master’s degree and two (2) years of experience in a quantitative/analytical/STEM field
OR Ph.D. in a quantitative/analytical/STEM field
Two (2) years of working experience in Machine Learning, Deep Learning, or Artificial Intelligence
Two (2) years of working programming experience in Python, R, Matlab, SAS or Java
One (1) years of working experience in Big Data Technology in Hadoop, Hive, Impala, Spark, or Kafka

Preferences

PhD
Background in banking and/or other financial services
Experience in Agile Software Development
Chartered Financial Analyst (CFA), Financial Risk Manager (FRM), or other relevant certifications

Skills and Competencies

A control-focused mind-set with strong process improvement capabilities
A proven track record of working in teams and of leading projects
Ability to manage multiple tasks and work effectively under pressure in a rapidly changing environment
Advanced Structure Query Language (SQL) skills
Attention to detail, initiative, and ability to work under tight deadlines
Comfortable with both relational databases and Hadoop-based data mining frameworks
Current knowledge of financial regulations
Excellent written and oral communication skills, especially clearly explaining quantitative concepts to non-quantitative audiences
Expertise in analyzing large, complex, multi-dimensional datasets using a variety of tools like Python or R.
Motivated, organized, and team-oriented
Proficient in visualization tools like Power Business Intelligencer (BI)
Strong business acumen with the ability to communicate with both business and Information Technology (IT) leaders
Understanding of statistical and predictive modeling concepts, machine learning approaches, clustering and classification techniques, and recommendation and optimization algorithms