Model Risk Analyst-Validation
$91,463–$101,463 year
On-site · Buffalo, New York, United States
Job Summary
Model Risk Analyst – Validation responsible for ensuring the accuracy, reliability, and regulatory compliance of models in accordance with Company or regulatory standards and policies. Perform validation and analysis of expert judgment or qualitative factors that augment quantitative models. Analyze financial data, trends, and regulations to identify potential opportunities for improvement. Develop new models to address changing risk environments. Monitor model performance, prepare reports for internal and external stakeholders, and document findings. Stay abreast of industry best practices and regulatory changes. Provide guidance and advice to other departments regarding model risk management. Prepare written summary and analysis of all validation work, using a combination of word processing and presentation software skills.
Required Qualifications
- Master’s degree in Applied Mathematics, Computing, Data Science, Materials Science, or related STEM field of study
- Three (3) years of experience as a Model Risk Analyst, Data Scientist, Quantitative Analyst, Product Developer, or related occupation
- Three (3) years of experience in Programming Languages (Python, MATLAB, C/C++, R, SAS)
- Three (3) years of experience in Technical Analyses (in-sample back-testing, moving averages, time series analysis, z-score analysis, performance metrics evaluation, goodness-of-fit tests (e.g., KS, Gini), correlation matrices)
- Three (3) years of experience in Technical Writing (Microsoft Word, LaTeX, Markdown, Git)
- Three (3) years of experience in Data Science concepts (statistics, probability, EDA, ML, model evaluation/selection, feature engineering, time series, loss forecasting, validation concepts such as cross-validation)
- Three (3) years of experience in Building Regression Models (Linear, Logistic, Multinomial) and handling multicollinearity
- Three (3) years of experience in Conducting independent review/validation of Machine Learning Models
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