Data Scientist
3 weeks ago
Key Responsibility Area : (Specifies Key Result Areas for the Apply predictive modeling techniques, including decision trees, regression analysis, and machine learning, to develop models that meet predictive accuracy requirements.
- Evaluate the conceptual and technical soundness of valuation and risk models according to OCC SR 11-7
- Model Risk Management guidelines.
- Critically assess modeling approaches, frameworks, assumptions, and testing methodologies, covering
- Performance testing, back testing, benchmarking, and stress/sensitivity testing.
- Perform data extraction from relational databases using SQL and employ programming languages like
- Python and R for model testing, data analysis, and generating reports.
- Ensure model documentation adheres to regulatory standards, particularly SR 11-7 guidelines, covering data analysis, model methodology, performance evaluation, and implementation.
- Execute risk analysis to validate the performance of existing risk models, lead gap-closing initiatives, and recommend improvements.
- Independently review model validations, assess identified issues, and propose solutions that meet methodological rigor.
- Generate regular reports on model performance monitoring, including updates on compliance with policies and procedures, progress, challenges, and issues.
- Provide recommendations for model design, development, back testing, implementation, and recalibration processes.
- Assist in model performance reviews, validate controls, and ensure effective communication with stakeholders about validation results.
- Develop and maintain a comprehensive and accurate model inventory in alignment with Model Validation Policy Criteria : (Skill set required to do the job- Knowledge, Tools, Technical Knowledge, Certifications etc.)
- Proficiency in predictive modeling techniques, including decision trees, regression analysis, and machine learning.
- Strong programming skills, especially in SQL, Python, and R, for model testing and data analysis.
- Proficiency in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and also experience with big data platforms (e.g., Spark, Hadoop).
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for data storage and model deployment.
- Thorough understanding of model risk management and regulatory standards, particularly OCC SR 11-7 guidelines.
- Expertise in model documentation, including methodologies, data analysis, and performance assessment.
- Experience in risk analysis and gap-closing initiatives for model validation.
- Competence in performing model performance testing, including back testing, benchmarking, and stress/sensitivity testing.
- Ability to effectively communicate validation results and model performance insights to stakeholders.
(ref:hirist.tech)-
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