APP Sierra - Model Validation & Implementation Analyst (2-8 yrs)

About the Role :

We are looking for a skilled Model Development & Validation Analyst to join our Risk Management and Analytics team. This role will be critical in supporting the development, implementation, and validation of statistical models used across the banking and credit cards domain. The ideal candidate will have a strong background in data science, risk analytics, and model validation processes, as well as experience in the financial sector.

Key Responsibilities :

1. Model Validation & Review :

- Perform independent validation of models, including stress testing, back-testing, and sensitivity analysis, to assess the accuracy, reliability, and stability of models.

- Evaluate the assumptions, methodologies, and data inputs used in model development and ensure they comply with regulatory standards and best practices (e.g., Basel, SR11-7).

- Identify, document, and communicate model limitations and areas for improvement, working with developers to enhance model performance.

- Prepare comprehensive validation reports that summarize findings, methodologies, and validation conclusions for regulatory and audit purposes.

2. Model Implementation :

- Collaborate with data scientists and model developers to implement statistical models for credit risk assessment, fraud detection, and customer behavior analysis.

- Ensure accurate and efficient deployment of models into production systems, working closely with IT and engineering teams to integrate models into banking platforms.

- Conduct model performance testing to ensure stability, scalability, and robustness in real-world scenarios.

- Develop automated scripts and tools to streamline model deployment and monitoring processes.

3. Data Analysis & Insights :

- Extract, clean, and analyze large datasets from various sources to support model development and validation activities.

- Conduct statistical analysis and data profiling to understand data patterns, trends, and anomalies.

- Assist in the development of predictive models for credit scoring, customer segmentation, and risk assessment, using advanced machine learning techniques.

4. Regulatory Compliance & Documentation :

- Ensure all models comply with regulatory requirements, such as Basel, OCC, Federal Reserve, and other applicable regulations.

- Maintain detailed documentation for all model implementation and validation activities, including methodologies, data sources, assumptions, and findings.

- Support internal and external audits by providing necessary documentation, reports, and explanations related to model implementation and validation.

5. Cross-Functional Collaboration :

- Work closely with teams across risk management, data science, IT, finance, and compliance to support the end-to-end model lifecycle.

- Participate in meetings with stakeholders to present validation results, address concerns, and align on action plans for model improvements.

- Provide recommendations to business units on model usage, limitations, and potential enhancements.

Qualifications :

- Education : Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related field. Advanced degrees are preferred.

Experience :

- 2+ years of experience in model implementation, validation, or risk analytics, preferably within the banking or credit card industry.

- Hands-on experience with statistical modeling, machine learning algorithms, and data analysis.

- Familiarity with credit risk modeling, fraud detection models, or customer behavior analytics is a plus.

Technical Skills :

- Proficiency in programming languages such as Python, data analysis and model implementation.

- Experience with database management systems (e.g., SQL) and data visualization tools (e.g., Tableau, Power BI).

- Strong understanding of statistical and machine learning concepts, including regression, classification, clustering, and neural networks.

Analytical Skills :

- Strong problem-solving skills with the ability to analyze complex datasets and extract meaningful insights.

- Experience with statistical testing, hypothesis testing, and data validation techniques.

Communication & Collaboration :

- Excellent written and verbal communication skills to present technical findings to non-technical stakeholders.

- Ability to work effectively in a collaborative team environment, managing multiple projects and deadlines.