Data Scientist
Job Description
About Paymob
Paymob is a leading FinTech company revolutionizing payment solutions in the MENA region. At Paymob, we believe in enabling businesses to thrive by offering secure, innovative, and scalable tools that simplify payments for everyone.
We’re proud to power millions of transactions for some of the biggest names in the region, with a team of innovators spread across Egypt, UAE, KSA and Oman.
We foster a value driven culture focused on continuous learning, empowering our people, and collaborating as one global team to create real impact.
Main Job Purpose
Utilizing analytical, statistical, and programming skills to collect, analyze, and interpret large data sets, then using this information to develop data-driven solutions to solve difficult business challenges using machine learning models. Exploring large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different solutions.
Responsibilities
- Partner with Growth, Commercial, Product and Operations teams to frame business problems as data science problems and agree how success will be measured.
- Build merchant-level models for segmentation, scoring, churn prediction (including detecting volume moving to competitors before an account goes inactive) and expected-vs-actual performance, accounting for seasonality.
- Design and analyse controlled experiments and campaigns (holdout groups, treatment vs control, uplift), and report impact on business outcomes such as Net Revenue Retention net of incentive cost.
- Build and maintain analytics data models in dbt (staging, warehouse and mart layers), with tests and documentation, across our Redshift and Trino/Iceberg warehouses.
- Develop and evaluate LLM-based features: retrieval-augmented Q&A, natural-language-to-SQL analytics, agent workflows and structured document extraction. Measure quality, cost and latency, and improve them.
- Validate data integrity before it is used: reconcile figures, detect duplication and staleness, and trace discrepancies to their source.
- Deploy models and pipelines to production, monitor model performance, data drift and data quality, and act on what monitoring shows.
- Present findings to technical and non-technical audiences, including executives, clearly and with the limits of the evidence stated.
- Contribute to team standards through code review, documentation and knowledge sharing.
Requirements
- Bachelor’s degree in computer science, Communications Engineering
- 1 to 2 years of hands-on experience in data science or applied machine learning, with at least one model or analysis that reached production or drove a business decision.
- Strong SQL: complex joins, window functions and query performance on large datasets.
- Strong Python for data work (pandas, scikit learn, gradient boosting libraries such as XGBoost or LightGBM) and for production code (tests, version control, code review).
- Solid statistics: hypothesis testing, experimental design, sample sizing, and awareness of causal inference and uplift modelling.
- Experience with classification, regression, clustering and time-series problems, and knowing which one a business question needs.
- Hands-on experience building with LLMs: prompt design, evaluation, RAG or vector search, or structured extraction.
- Experience with cloud data warehouses and modern data tooling such as Redshift, Trino, Iceberg, S3, dbt and Airflow.
- Experience with a BI tool such as Superset, Tableau, Power BI or similar.
- Clear written and spoken English. Arabic is a strong plus.
Nice to Have
- Payments, fintech, banking or e-commerce domain experience (merchant acquiring, transaction data, churn, lifetime value).
- CRM, customer value management or marketing analytics experience, including campaign measurement.
- Docker, CI/CD and API development (for example FastAPI).
- Document AI or OCR experience, especially with Arabic text.
- Work experience in training neural networks (including CNNs, GNNs, RNNs, Transformers)
Benefits
Why You’ll Love It Here
At Paymob, our people are at the core of everything we do. Our culture is built around values that drive how we work, grow, and succeed together:
We offer competitive compensation packages and performance driven incentive programs.
We offer a flexible hybrid work model that supports both in office collaboration and remote productivity.
We invest in continuous career coaching to support your growth and long-term development.
We collaborate across borders, embracing diversity and shared success.