Head of Credit Data Science Leader
3 weeks ago
About the Role
The Head of Credit Data Science will lead the design, development, and improvement of credit models, scorecards, and analytics tools to enhance Lula's automated credit decisioning systems.
Responsibilities
- Develop and execute a strategic roadmap for credit automation aligned with business objectives.
- Collaborate with the Credit Management team to define vision and goals for credit automation initiatives.
- Identify expansion opportunities to grow the portfolio profitably.
- Work with cross-functional teams to ensure credit models are optimised for both performance and compliance.
- Implement and monitor controls to mitigate potential risks associated with automated credit decisions.
- Ensure accurate and timely reporting on the performance of automated products.
- Develop and maintain an IFRS-compliant provisioning model in collaboration with Finance and Credit Management.
- Continuously evaluate and develop analytical models to support funding decisions.
- Provide input to enhance existing processes and identify new opportunities within credit and collections teams.
- Collaborate with the Portfolio Manager to analyse portfolio performance and recommend remedies.
- Stay up-to-date with AI/ML trends and methodologies to identify emerging credit risks and opportunities.
Qualifications
- Strategic thinker with the ability to align model development to business goals.
- Strong leadership and team-building capabilities.
- Excellent communication skills, with the ability to present complex data insights to non-technical stakeholders.
- High attention to detail, ensuring accuracy and compliance in all modelling activities.
- Passion for innovation and staying ahead of emerging trends in data science and credit automation.
Requirements
- 10+ years of experience in data science, analytics, or related roles within financial services, ideally in lending or fintech.
- Proven experience leading teams and driving strategic initiatives to implementation in a data-driven environment.
- Significant experience in the implementation of credit risk models and scorecards, with previous accountability for the performance of these models in the credit context.
- Strong expertise in machine learning, AI, and statistical modelling techniques.
- Proficiency in programming languages like Python, R, and SQL.
- Experience working with credit risk and decisioning platforms.
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