About the company
GoTyme Bank is a partnership with FNB, providing Merchant Cash Advances (MCAs) to SME merchants.
Responsibilities
- Own the end-to-end credit risk management of the FNB MCA portfolio, independently driving the work plan while collaborating with the wider Data Science and Risk teams.
- Monitor portfolio performance, identify trends and emerging risks or opportunities, and recommend appropriate risk mitigation or growth actions.
- Set and manage the portfolio’s credit risk strategy, policy, and decisioning rules, balancing risk, growth, and commercial objectives within agreed risk appetite.
- Develop, maintain, implement, and monitor scorecards and other credit risk models and methodologies across the credit lifecycle.
- Ensure that all data science models are developed, reviewed, documented, and governed in line with the bank’s model risk management standards.
- Support independent model validation activities by providing clear development documentation, data definitions, assumptions, limitations, methodology rationale, and performance results.
- Work with data engineering and technology teams to support the deployment of models and decisioning logic into production environments, including implementation testing and reconciliation.
- Collaborate with cross-functional stakeholders, including Finance, Operations, and Compliance, to align portfolio and risk management objectives.
- Prepare and present reports to senior management, highlighting key risk metrics, trends, and recommendations.
- Develop stress testing scenarios and sensitivity analyses to assess the resilience of the FNB MCA portfolio under various economic conditions.
- Monitor changes to regulatory frameworks, particularly IFRS 9 requirements, and ensure compliance in credit risk management practices and reporting.
Requirements
- Degree in mathematics, statistics, data science, or a related quantitative field.
- 3–5 years of credit risk analysis or data science experience in banking, lending, fintech, or financial services, including independent delivery of credit risk work.
- Strong understanding of credit risk strategy, scorecards, models, and machine learning techniques for credit decisioning.
- Familiarity with IFRS 9 principles and their application to credit risk management, including expected credit loss (ECL) calculations.
- Strong analytical skills with the ability to interpret complex financial and trade data and make informed decisions.
- Proficiency in SQL and Python is essential; experience with Databricks is advantageous.
- Excellent communication and presentation skills, with the ability to convey complex concepts to non-technical stakeholders.
- Ability to work independently, structure ambiguous problems, take ownership, and manage multiple priorities effectively.
- Strong testing discipline, including data quality checks, reconciliation, and implementation testing.
- Familiarity with SME lending, including industry-specific risk factors, is highly desirable.
- Proficiency in advanced analytics and the responsible use of AI tools is essential.
Conditions