Data Scientist

Job description

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

o Apply predictive modeling techniques, including decision trees, regression analysis, and machine learning,
to develop models that meet predictive accuracy requirements. 

o Evaluate the conceptual and technical soundness of valuation and risk models according to OCC SR 11-7
Model Risk Management guidelines. 

o Critically assess modeling approaches, frameworks, assumptions, and testing methodologies, covering
performance testing, back testing, benchmarking, and stress/sensitivity testing. 

o Perform data extraction from relational databases using SQL and employ programming languages like
Python and R for model testing, data analysis, and generating reports. 

o Ensure model documentation adheres to regulatory standards, particularly SR 11-7 guidelines, covering
data analysis, model methodology, performance evaluation, and implementation. 

o Execute risk analysis to validate the performance of existing risk models, lead gap-closing initiatives, and
recommend improvements. 

o Independently review model validations, assess identified issues, and propose solutions that meet
methodological rigor. 

o Generate regular reports on model performance monitoring, including updates on compliance with policies
and procedures, progress, challenges, and issues. 

o Provide recommendations for model design, development, back testing, implementation, and recalibration
processes. 

o Assist in model performance reviews, validate controls, and ensure effective communication with
stakeholders about validation results. 

o 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.) 

o Proficiency in predictive modeling techniques, including decision trees, regression analysis, and machine
learning. 

o Strong programming skills, especially in SQL, Python, and R, for model testing and data analysis. 

o Proficiency in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and also experience with
big data platforms (e.g., Spark, Hadoop). 

o Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for data storage and model deployment. 

o Thorough understanding of model risk management and regulatory standards, particularly OCC SR 11-7
guidelines. 

o Expertise in model documentation, including methodologies, data analysis, and performance assessment. 

o Experience in risk analysis and gap-closing initiatives for model validation. 

o Competence in performing model performance testing, including back testing, benchmarking, and
stress/sensitivity testing. 

o Ability to effectively communicate validation results and model performance insights to stakeholders.