mylo is a fintech platform dedicated to helping millions of people and businesses thrive by providing accessible and responsible financial solutions. Whether you’re purchasing a mobile phone, a new jacket, a flight ticket, a comfy couch, or even covering school tuition, mylo enables you to buy now and pay later at thousands of points of sale across Egypt. Born out of B.TECH—Egypt’s leading electronics and appliances retailer with over 27 years of experience in offering buy now, pay later solutions—mylo brings a legacy of trust and innovation to the fintech space. All mylo products are fully Sharia-compliant, ensuring ethical and inclusive financial practices.
We are looking for a Junior Data Scientist with a strong engineering mindset who is eager to evolve beyond "notebook data science." In this role, you will work across the full consumer finance lifecycle—analyzing data for Growth, Pricing, and Risk—while being trained on a modern, high-performance MLOps stack. You will learn to treat data science code as production software.
Responsibilities:
- End-to-End Modeling: Assist in training and tuning models for various business domains using modern Python libraries.
- Engineering Integration: Work with the team to expose models via APIs. You will learn to implement Feature Store definitions and ensure data quality for real-time serving.
- Data Operations: Handle data preparation and analysis using SQL and Python. Learn to manage datasets using Data Version Control tools to keep track of changes.
- Code Quality: Write clean, modular, and tested code. You will participate in code reviews and use version control (Git) as part of your daily workflow.
- Continuous Learning: Participate in our induction program to master our specific tools for model serving, package management, and system monitoring.
Requirements
- Education: B.Sc. in Computer Science / Engineering, Statistics, Mathematics, or a relevant quantitative field.
- Technical ML Foundation:
- Algorithms: Solid conceptual and practical understanding of Classification (Logistic Regression, Decision Trees, Random Forests) and Regression analysis.
- Deep Learning: Basic understanding of Neural Networks architectures and principles (e.g., activation functions, loss functions, backpropagation).
- Libraries: Hands-on familiarity with Scikit-Learn for preprocessing, model selection, and pipelines.
- Optimization: Exposure to hyperparameter tuning concepts and gradient boosting frameworks (e.g., LightGBM or XGBoost).
- Software Engineering Fundamentals:
- Version Control: Strong familiarity with Git commands (branching, merging, resolving conflicts) and collaboration platforms (GitHub/GitLab).
- Code Quality: Ability to write clean, reusable, and readable code (not just scripts). Understanding of functions, modularity, and basic testing.
- Core Skills: Strong grasp of Python programming and SQL.
- Analytical Foundation: Solid understanding of statistics and standard data manipulation libraries (Pandas, NumPy).
