Quantitative Analyst

Pepkor Lifestyle; Sandton; On-site; ZAR 600,000 - 1,000,000; Full time

Job summary

Pepkor Lifestyle is seeking a Quantitative Analyst to build predictive credit risk models and support decision-making in the Credit Analytics department. The role involves data preparation, model development, and monitoring across Originations processes.

You will work with SAS and ML techniques, with 4–10 years of credit risk experience preferred. Compliance with SA regulations and collaboration with stakeholders are essential for success in this role.

Qualifications

• Bachelor's degree in Statistics, Business Mathematics, or Risk Management is required.
• Post-graduate qualification is advantageous.
• 4 to 10 years of Credit Risk experience.
• Proven statistical model-building experience and SAS usage.
• Experience in ML techniques is highly advantageous.

Responsibilities

• Model Data Preparation: Select and prepare relevant data, define and obtain agreement on a model's outputs, and obtain data approval before development.
• Build Statistical Models: Conduct analytics and validations, and obtain approval at the model technical committee.
• Manage Development: Create Originations system documentation, oversee testing, and drive policy change management.
• Define Strategy: Develop metrics to measure impact of Originations strategy and ROI, and optimize solutions.

Skills

Risk Management

Information processing

Written and verbal communication

Adaptability

Analytical skills

Education

Bachelor's degree in Statistics, Business Mathematics, or Risk Management (NQF Level 7)

Post-graduate qualification advantageous

Tools

SAS

Machine Learning

Job description

Job Opportunity: Quantitative Analyst

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.