Machine Learning Engineer

Role overview Develop and deploy machine learning systems for banking use cases, including fraud prevention, credit risk assessment, customer segmentation, and personalization. You will collaborate across data, product, and compliance teams, with attention to model performance, regulation, and ethical AI practices.

Responsibilities - Design, train, evaluate, and deploy models for banking applications. - Build fraud detection and prevention approaches, as well as credit risk and scoring models. - Develop segmentation and personalization models and supporting ML pipelines. - Work with large datasets, feature engineering, and data quality. - Monitor deployed models and refine them as performance or needs change. - Document methods and communicate findings to technical and non-technical stakeholders.

Requirements - At least three years in machine learning or data science. - Strong programming ability in Python or R and experience with TensorFlow, PyTorch, or scikit-learn. - Knowledge of statistical modeling, evaluation methods, data processing, and feature engineering. - Understanding of MLOps and model deployment. - Strong analytical, problem-solving, and communication skills. - Bachelor's or master's degree in computer science, statistics, or a related field.

Nice to have Banking or fintech experience; familiarity with fraud detection or credit risk modeling; and experience with managed cloud machine learning platforms. Knowledge of responsible AI and relevant regulation is useful.

Benefits and work setup Full-time role with remote or hybrid work options in Dakar, Senegal. The role description cites professional development and a focus on ethical finance.