Machine Learning Engineer / Data Scientist
Technology
Nairobi HQ, Kenya
We're looking for a passionate Machine Learning Engineer or Data Scientist who is excited about building predictive models that have real-world impact in fintech. You'll work on developing and improving credit risk models, particularly probability of default (PD) models, while aggregating and analyzing data from multiple sources. This is a role for someone who loves working with data, is curious about machine learning, and wants to continuously learn and grow.
Responsibilities
● Building and improving probability of default (PD) models for credit assessment
● Aggregating and processing data from multiple sources (MongoDB, SQL databases, APIs, external data providers)
● Designing and implementing end-to-end ML pipelines including data collection, preprocessing, feature engineering, model training, and deployment.
Developing data pipelines to collect, clean, and transform data for modeling
● Working with financial data and credit scoring systems
● Creating features and performing feature engineering for ML models
● Evaluating model performance and iterating improvements
● Collaborating with the development team to integrate models into production
● Conducting research and experimentation with advanced ML techniques such as ensemble methods, deep learning, and time-series forecasting for credit risk prediction.
Requirements
● 3-5 years of experience in machine learning, data science, or a related quantitative field
● Strong Python experience (this is essential)
● Experience with machine learning frameworks - Required: scikit-learn, XGBoost, LightGBM; Nice to have: TensorFlow, PyTorch, Keras
● Experience with credit risk modeling is a strong plus but not mandatory
● Experience working with multiple data sources and data integration
● Familiarity with APIs - both consuming and building them
● Knowledge of statistical analysis and model evaluation techniques
● Experience with data manipulation libraries (pandas, numpy)
● Understanding of databases - Must-have: MongoDB, PostgreSQL; Nice to have: MySQL, Redis
● Knowledge of data versioning, model versioning, and MLOps pipelines is a plus