Senior Data Scientist (Credit) at Moniepoint Incorporated
Job Description
Moniepoint Incorporated is seeking an experienced Senior Data Scientist (Credit) to support consumer lending decisions through advanced analytics, machine learning, and credit risk modeling. This role involves developing data-driven solutions that improve underwriting, pricing, customer acquisition, retention, and portfolio performance. The successful candidate will work closely with credit, product, and data teams to deliver scalable models and actionable insights that drive business growth.
Key Responsibilities
- Develop and deploy credit scoring, affordability, and behavioral models to support lending decisions, pricing strategies, and collections activities.
- Design, execute, and analyze experiments to optimize approval rates, reduce loss rates, and improve profitability.
- Collaborate with product teams to integrate decision-making models and business logic into real-time systems.
- Build predictive models that improve collections performance, customer retention, and churn management outcomes.
- Ensure model governance, data quality, compliance, and ethical use of data across decision-making processes.
- Provide data-driven insights and recommendations that support risk ranking and personalized product offerings.
- Mentor cross-functional teams on experimentation frameworks and evidence-based decision making.
- Contribute to the development and deployment of credit risk models while evaluating the effectiveness of machine learning and traditional analytical approaches.
Job Requirements
Interested candidates should possess:
- A degree or professional qualification in Statistics, Mathematics, Engineering, or another quantitative discipline.
- At least 5 years of experience in data science, decision science, risk analytics, or a related field within financial services.
- Strong understanding of credit risk management, consumer lending practices, and relevant regulatory considerations.
- Proficiency in SQL and at least one programming or modeling language such as Python or R.
- Experience with machine learning, A/B testing, collections modeling, and churn prediction techniques.
- Strong analytical and problem-solving skills with the ability to translate complex data into business recommendations.
- Excellent communication and stakeholder management abilities.
- A proactive mindset with the ability to thrive in a fast-paced, cross-functional environment.
Benefits
- Competitive salary package.
- Pension contribution.
- Health insurance coverage.
- Monthly bonuses and additional employee benefits.
- Learning and development opportunities.
- Knowledge-sharing culture and regular technical training sessions.
- Inclusive and collaborative work environment.
Hiring Process
Candidates can expect the following stages during the recruitment process:
- Preliminary phone interview with a recruiter.
- HackerRank assessment covering data science fundamentals, mathematics, statistics, linear algebra, and Python.
- Take-home assignment.
- Technical interview with a Data Science Team Lead to review the assignment.
- Behavioral and technical interview with the hiring manager.
