Data Science Lead
Paystack · Dubai, AE ·
- Work mode
- Hybrid
- Seniority
- Lead
- Category
- Data science
- Experience
- 5+ years
Paystack · Dubai, AE ·
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.
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.
Advanced Analytics & Model Ownership
Strategy & Roadmap: Define the advanced analytics vision by leveraging business acumen and stakeholder relationships to prioritize high-impact projects.
Infrastructure: Identify and source the tooling and frameworks required to scale advanced analytical capabilities.
Risk & Forecasting: Develop and maintain core credit risk models (PD/LGD/EAD, default, segmentation) and business forecasting frameworks (revenue, volume, margins) under varying scenarios.
Optimization: Ensure model integrity through continuous monitoring, back-testing, stress-testing and documentation for auditors and regulators.
Data Lifecycle & Collaboration
Extract and refine datasets for modeling, partnering with Data Engineering to automate pipelines and ensure rigorous data governance.
Passion: Have a deep interest and curiosity in advanced analytics.
Collaborative: Have exceptional stakeholder management abilities.
Resilient: Do whatever it takes to make your team successful, no matter the issue—whether sourcing data or getting strategic buy in.
Communication: Are an excellent and empathetic communicator both verbally and in writing.
Required Skills & Experience:
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
Competitive compensation package and benefits
13th month bonus
TSG Equity compensation
Full medical coverage
Wellbeing stipend
Generous leave and sabbatical policies
Hybrid working environment
Smart, kind colleagues who are invested in your growth.
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.