Quantitative Analyst at Pepkor Lifestyle

Job Purpose

• The core purpose of this role is to build predictive models that will enable accurate business decision-making in the Credit Analytics department.

Key Responsibilities

• Model Data Preparation: Select and prepare relevant data, define and obtain agreement on a model's unique outputs, and motivate approval of data before model development commences.
• Build Statistical Models: Conduct required analytics and validations, and motivate for approval at the model technical committee meeting.
• Manage Development: Create Originations system business requirement documentation, oversee system testing, and drive change management for Originations policies, procedures, and processes.
• Define Strategy: Develop and implement metrics to measure the impact of Originations strategy, analyze Return on Investment (ROI), and collaborate with the team to optimize solutions.
• Risk Monitoring & Controls: Design exception reports to highlight policy gaps and implement appropriate system validations to prevent data manipulation.
• Bureau and Industry Analytics: Obtain necessary analysis to identify opportunities, highlight threats, and report on any identified risks.
• Model Implementation: Engage with key stakeholders to ensure model coding aligns with technical specifications, complete change request documentation, and participate in pre- and post-implementation testing.
• Monitoring & Calibration: Monitor the effectiveness of implemented models, drive successful Champion Challenger scenarios, and track the operational efficiency and health levels of all scorecards on a regular basis.
• Self-Management & Teamwork: Maintain high standards of professionalism, apply organizational knowledge to achieve results, and show commitment to teamwork.

Knowledge

• Knowledge of credit risk best practice and methodologies.
• Knowledge of collections, credit granting, and risk methodologies, processes, and systems.
• Knowledge of best practice scorecard development.
• Understanding of relevant legislation, including the NCA, Debt Collection Act, and CPA.
• Knowledge of SAS and Machine Learning.

Skills

• Risk Management.
• Information processing.
• Written and verbal communication skills.
• Adapting and responding to change.
• Good analytical and problem-solving skills.

Behaviours

• Adaptability.
• Honesty and integrity.
• Willingness to learn and improve.
• Proactiveness.
• Responsibility and Accountability.

Minimum Qualifications & Experience

• A Bachelor's degree in Statistics, Business Mathematics, or Risk Management (NQF Level 7).
• A Post-graduate qualification is advantageous.
• 4 to 10 years of Credit Risk experience.
• Proven statistical model-building experience.
• SAS experience.
• Experience in Machine Learning (ML) techniques is highly advantageous.