Head of Credit Data Science Lead

2 weeks ago

Cape Town, Western Cape, South Africa Lula Full time
Job Title: Head of Credit Data Science

At Lula, we're seeking a seasoned data science leader to spearhead our credit data science initiatives. As the Head of Credit Data Science, you'll be responsible for driving the development and implementation of credit models, scorecards, and analytics tools to enhance our automated credit decisioning systems.

Key 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 the Head of Credit Underwriting and Portfolio Manager to combine data-driven insights with credit knowledge.
  • Accountable for the implementation and performance of automated credit decisioning models (across multiple products, including Merchant Cash Advance and revolving credit for SMEs) in line with KPIs and Business Strategy.
  • Monitor and enhance automated credit decisioning models and ensure necessary improvements.
  • Implement and monitor controls to mitigate potential risks associated with automated credit decisions.
  • Ensure accurate and timely reporting on the performance of automated products.
  • Responsible for building, implementing, and maintaining an IFRS-compliant provisioning model in collaboration with Finance and Credit Management.
  • Continuously evaluate and develop analytical models to support funding decisions (credit risk, affordability, pricing, delinquency, and fraud detection).
  • Ensure appropriate governance for development, sign-off, and monitoring of models.
  • Provide input to enhance existing processes, identifying new opportunities within credit and collections teams.
  • Collaborate with the Portfolio Manager to analyse portfolio performance, identify higher-risk areas, and recommend remedies.
  • Extend company data with relevant third-party and alternative sources for building analytic systems.
  • Stay updated with AI/ML trends and methodologies to identify emerging credit risks and opportunities.
  • Work closely with Data Engineering to ensure required data for credit modelling and reporting is available.
  • Collaborate with the Product team to prioritise and drive initiatives forward.
  • Manage the Data/Decision Science team, including recruitment, KPI setting, and ensuring world-class delivery.
Requirements:
  • 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.
  • A self-starter, able to take initiative and drive models from concept to implementation while ensuring usability across teams.
Preferred Qualifications:
  • Grade 12 achieved
  • Bachelor's or Master's in Data Science, Statistics, Mathematics, or a related quantitative field.
  • Advanced degree (PhD) preferred but not required.
  • 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, scorecards together 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.
  • Solid understanding of IFRS9 and ECL modelling would be beneficial.
  • Knowledge of cloud-based environments and large data systems (AWS, Azure, etc.).

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