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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.
- 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.
- 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
- 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