Data Scientist - R/Python (5-8 yrs)

Key Responsibility Area : (Specifies Key Result Areas for the Incumbent)

- 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 requirements.

Eligibility 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.