Senior Vice President - Risk & Analytics - FinTech

As a Senior Risk Analytics, you will play a crucial role in ensuring the organization's risk management strategies are data-driven and effective.

What you need to do :

- Conducting in-depth cohort analysis to understand and evaluate the performance of various customer segments over time.

- Identifying First Payment Default (FPD) and Straight Roller scenarios, and developing strategies to mitigate associated risks.

- Developing and implementing strategies for fraud identification, prevention, and mitigation through advanced analytics techniques.

- Utilizing data-driven methods to segment customers based on risk profiles, behavior, and other relevant criteria.

- Implementing risk-based pricing models to optimize lending strategies and enhance overall portfolio performance.

- Contributing to the development and optimization of credit line assignment strategies based on risk assessment.

- Conducting CHAID (Chi-squared Automatic Interaction Detection) analysis to identify significant relationships and patterns in customer data.

- Applying regression models, both logistic and linear, for risk assessment and forecasting purposes.

- Conducting fundamental data analysis using tables and crosstabs to extract meaningful insights.

- Performing Swap-In/Swap-Out analysis to understand customer behavior and assess the impact on risk dynamics.

- Demonstrating a solid understanding of Machine Learning (ML) models and their potential application in risk analytics.

What you need to have :

- Bachelor's/Master's degree in a quantitative discipline such as Statistics, Mathematics, Economics, or related field. Advanced degree or relevant certifications (e.g., CFA) preferred.

- 5-8 years of experience in risk analytics within the fintech or financial services industry.

- Proficiency in statistical analysis tools and programming languages such as R or Python.

- Strong expertise in cohort analysis, FPD identification, fraud prevention, and risk-based pricing strategies.

- Experience with CHAID analysis, logistic/linear regressions, forecasting, and basic data analysis techniques.

- Knowledge of Swap-In/Swap-Out analysis methodologies.

- Familiarity with Machine Learning models is a definite plus.