Pezesha, has created a holistic financial marketplace for MSMEs. By offering lending, financial education, and debt counselling to borrowers, plus a proprietary credit scoring system to vet MSMEs without a credit history, derisking lending to SMEs. Lower Risks bring commercial banks and capital providers onto Pezesha platform. As a collaborative structure, Pezesha is helping to tackle the $19 Billion financing gap for SMEs.
Pezesha is led by a highly experienced and passionate local team with more than 10 years local and international experience in fin-tech, management of growth and technology companies, and unparalleled local market knowledge and reach.
Role Overview
We are looking for a Senior Data Scientist to join our team and lead the development of advanced credit risk models, strategic insights, and business intelligence tools that drive high repayment rates, operational efficiency, and portfolio growth. This role is critical in strengthening our risk management framework and scaling our impact across the continent.
Core Responsibilities
Credit Risk Model Development
- Develop, test, and iterate credit risk models on a quarterly basis.
- Ensure repayment rates consistently exceed 95%, with a default rate of less than 3% across all portfolios.
Strategic Data Insights
- Provide data-driven insights and reports to portfolio leads, senior management, and external stakeholders.
- Translate findings into actionable strategies to enhance performance and mitigate risks.
Data Extraction, Cleaning & Mining
- Lead the end-to-end processes of data extraction, cleaning, transformation, and mining.
- Build datasets that power descriptive, predictive, and spatial models to support decision-making.
Cross-Functional Experimentation
- Design and lead experiments across business units to optimize products, processes, and risk strategies.
- Measure and communicate results to ensure learnings drive continuous improvements.
Business Intelligence Development
- Develop and enhance BI tools to maximize product efficiency, improve Lifetime Value (LTV), and gross margins.
- Deliver at least two major BI tool enhancements or new feature implementations per quarter.
Real-Time Dashboards & Reporting
- Build and maintain real-time dashboards with a 99% uptime and accuracy target.
- Deliver monthly finance reports on-time and with > 99% accuracy in reports. Continuously add to exception reports to enhance portfolio quality and leverage AI + automation to improve reporting processes.
- Ensure accurate and timely lender reporting, portfolio impact analysis, and internal business reporting.
Documentation & Transparency
- Maintain clear documentation of models, experiments, and workflows to ensure reproducibility and transparency.
Mentorship & Team Leadership
- Mentor junior data scientists and analysts, fostering a collaborative, learning-driven culture.
- Lead the data science team in setting priorities, managing workloads, and delivering high-quality outputs.
Task Delegation & Ad Hoc Priorities
- Execute tasks assigned by the CEO, COO, Data Science Manager or CDO, prioritizing based on organizational goals and impact.
Key Performance Indicators (KPIs)
- LMS Accuracy: Achieve and maintain ≥ 98% accuracy in Learning Management System data validation.
- Sprint Completion: Deliver 90%+ completion rate for planned sprints on time.
- SLA Adherence: Maintain 95%+ adherence to Service Level Agreements (SLAs) across data services.
- Knowledge Transfer: Deliver and track quarterly KT sessions and training for the data team.
- Credit Risk Model Performance: Maintain 95% repayment rate and < 3% default rate via model iterations.
- Business Intelligence: Deliver at least 2 new BI enhancements/features per quarter to improve reporting and decision-making.
Qualifications & Skills
- Master’s or higher in Data Science, Statistics, Computer Science, Economics, or related field (MBA/MSc/MA, PhD/Fellowship).
- 6+ years of proven experience in data science, machine learning, and credit risk modeling (preferably in fintech or financial services).
- Strong expertise in Python, R, SQL, and big data tools.
- Experience with machine learning frameworks (TensorFlow, Scikit-learn, XGBoost, etc.).
- Proficiency in data visualization tools (PowerBI, ggplot, or plotly).
- Strong understanding of credit risk management, repayment behavior, and portfolio analytics.
- Excellent leadership, mentoring, and cross-functional collaboration skills.
- Strong communication skills with the ability to translate complex data into actionable insights.
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