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About the Role
We are seeking a high-energy Lead Data Scientist with 5+ years of experience in predictive modeling to drive measurable bottom-line impact. Reporting to the Data Analytics Lead, you will own credit risk analytics (Paystack MFB), business forecasting, and strategic predictive projects. You will collaborate with the broader data team and business heads to deploy high-quality, meticulously crafted analytical products that solve complex business challenges.
Responsibilities
- Define the advanced analytics vision by leveraging business acumen and stakeholder relationships to prioritize high-impact projects.
- Identify and source the tooling and frameworks required to scale advanced analytical capabilities.
- Develop and maintain core credit risk models (PD/LGD/EAD, default, segmentation) and business forecasting frameworks (revenue, volume, margins) under varying scenarios.
- Ensure model integrity through continuous monitoring, back-testing, stress-testing and documentation for auditors and regulators.
- Extract and refine datasets for modeling, partnering with Data Engineering to automate pipelines and ensure rigorous data governance.
Requirements
- Master’s in a quantitative field (Statistics, Economics, Data Science, or similar).
- 5+ years in predictive modeling or data science, ideally within fintech or lending.
- Expert in statistical modeling and time-series forecasting with a proven track record of implementation.
- Advanced SQL for ETL/analysis and expert Python (Pandas, NumPy, Scikit-learn, Statsmodels).
- Experience in building credit risk models (PD, LGD, EAD)
- Experience building and maintaining robust model pipelines.
- Proven ability to translate complex quantitative results into clear, actionable insights for non-technical stakeholders.
- Experience in building application and behaviour scorecards in the credit risk space and in influencing lending decisions
- Experience with dbt, Airflow for orchestration and GitHub for change control
- Experience in using AI to improve quality and efficiency of workflows
Skills
- Predictive modeling
- Credit risk analytics
- Business forecasting
- Statistical modeling
- Time-series forecasting
- SQL
- Python
- Pandas
- NumPy
- Scikit-learn
- Statsmodels
- Model pipelines
- Stakeholder management
- Data governance
- dbt
- Airflow
- GitHub
- AI
Location
- Hybrid
Work Type
- Hybrid
Experience Level
- 5+ years
Education Level
- Master’s in a quantitative field (Statistics, Economics, Data Science, or similar)
Benefits
- Competitive compensation package and benefits
- 13th month bonus
- TSG Equity compensation
- Full medical coverage
- Wellbeing stipend
- Generous leave and sabbatical policies
- Smart, kind colleagues who are invested in your growth.
About the Company
- Paystack’s mission is to power African ambition. Over 300K businesses across Nigeria, South Africa, Ghana, and Kenya use Paystack’s modern payments gateway, including Qatar Airways, MTN, Burger King, UPS, Africa World Airlines, AXA Mansard Insurance, FairMoney, PiggyVest, Crocs, Under Armour, Richemont Lifestyle Group, and many others.
- Over the last 10 years, we’ve built products that have helped shape online payments in Africa, from automated recurring payments to direct bank payments and automated chargebacks. Today, Paystack is part of The Stack Group (TSG), a family of technology brands building modern infrastructure across payments, banking, consumer products, and emerging technologies.
- At Paystack, we hire talented people, treat them with genuine respect, and give them the space, resources, and support to do the best work of their lives. We’d love your help.
Equal Opportunity
- Paystack is an equal opportunity employer and prohibits discrimination and harassment of any kind. We’re committed to providing employees with a work environment that is progressive and open-minded. Our employment philosophy is to hire the best people and empower them to do the best work of their lives. Employment decisions are based on business needs and individual merit without regard to race, color, religion, ethnicity, sexual orientation, nationality, marital status, gender, or age.