Senior Data Scientist

Moniepoint Incorporated, a global business payments and banking platform and one of Africa's leading fintech unicorns, is recruiting a remote Senior Data Scientist (Credit). In this high-impact role, you will build, deploy, and refine the core machine learning models, credit scoring algorithms, and experimentation pipelines powering consumer lending decisions across Africa.

• Credit Risk Modeling: Design, build, and deploy end-to-end credit scoring, affordability assessment, behavioral, and underwriting models to support consumer credit decisioning.

• Real-time System Integration: Partner with product squads and engineering teams to embed decision logic and ML models directly into real-time transactional systems.

• Portfolio & Collections Optimization: Build machine learning models focused on risk ranking, dynamic credit limits, personalized pricing, churn prediction, and collection strategies.

• A/B Testing & Experimentation: Design and execute statistical experiments to optimize approval rates, manage default/loss rates, and maximize portfolio profitability.

• Ethics & Model Governance: Ensure model compliance, statistical fairness, data quality, and ethical AI practices across all automated credit decisioning frameworks.

• Degree: Bachelor's or Master's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, Decision Science, or equivalent).

• Years of Experience: Minimum of 5+ years of hands-on data science, decision science, or risk analytics experience in financial services or fintech.

• Domain Expertise: Solid working knowledge of credit risk modeling, consumer lending mechanics, collections analytics, and regulatory considerations in banking.

• Programming & Tools: Advanced proficiency in SQL and Python (or R) for data analysis, machine learning pipelines, and statistical modeling.

• Statistical Methods: Demonstrated experience with A/B testing, hypothesis testing, machine learning algorithms, linear algebra, and statistical inference.

• Soft Skills: High ownership mindset, excellent cross-functional communication, and the ability to translate complex statistical concepts into clear strategic recommendations for business leaders.

1. Recruiter Screen: Preliminary call with the talent acquisition team.

2. HackerRank Assessment: Technical exercise covering core data science theory (mathematics, statistics, linear algebra) and Python fundamentals (data structures & algorithms).

3. Take-Home Assignment: Practical credit risk data challenge.

4. Technical Deep Dive Interview: Review and discussion of your take-home assignment with a Lead Data Scientist.

5. Hiring Manager Interview: Behavioral and strategic technical interview with the hiring manager.