AI and machine learning engineer
About the company
WayaBank is a digital bank designed around how people in Nigeria live and work, offering a full banking experience without queues, paperwork, or stress. Individuals can manage daily finances, while businesses can handle payroll, vendor payments, and growth from a single platform. Customers can open accounts in minutes, send and receive money instantly, pay bills, request loans, access POS and agency banking services, and manage everything from their phones, with no branch visit required. Guided by the conviction that every Nigerian deserves reliable, affordable, and dignified financial services, WayaBank positions itself as the bank of the new Nigeria. WayaBank is part of the WayaLinks ecosystem, alongside WayaGram and WayaQuick, providing integrated financial and digital services.
Your tasks
The AI and Machine Learning Engineer will design, build, and deploy data-driven models that enhance WayaBank’s digital banking products and customer experience. Day-to-day responsibilities include developing and training machine learning models for tasks such as fraud detection, credit scoring, personalization, and risk analysis, as well as implementing pattern recognition and neural network solutions in production systems. The engineer will collaborate with product, engineering, and data teams to translate business needs into scalable algorithms, optimize model performance, and monitor model behavior in real-time. The role also involves preparing and cleaning data, conducting statistical analyses, improving existing algorithms, and documenting technical solutions and best practices. This is a full-time hybrid role based in Lekki, with a mix of on-site collaboration and some work-from-home flexibility.
Requirements
- Strong foundations in Computer Science and Algorithms, including data structures, complexity analysis, and software engineering principles.
- Hands-on expertise in Neural Networks and Pattern Recognition, with experience applying deep learning and ML techniques to real-world problems.
- Solid understanding of Statistics for modeling, hypothesis testing, and performance evaluation of machine learning solutions.
- Proficiency with ML frameworks and tools such as Python, TensorFlow, PyTorch, scikit-learn, and modern data pipelines.
- Experience deploying models to production (e.g., REST APIs, microservices, cloud platforms) and monitoring their performance and reliability.
- Ability to work with large, complex datasets, including data preprocessing, feature engineering, and model optimization.
- Effective communication skills and the ability to collaborate with cross-functional teams in a fast-paced fintech environment.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
Other
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