Machine Learning Engineer / Data Scientist

We are 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 will 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.\n\n### Responsibilities\n- Building and improving probability of default (PD) models for credit assessment.\n- Aggregating and processing data from multiple sources including MongoDB, SQL databases, APIs, and external data providers.\n- Designing and implementing end-to-end machine learning (ML) pipelines including data collection, preprocessing, feature engineering, model training, and deployment.\n- Developing data pipelines to collect, clean, and transform data for modeling.\n- Working with financial data and credit scoring systems.\n- Creating features and performing feature engineering for ML models.\n- Evaluating model performance and iterating improvements.\n- Collaborating with the development team to integrate models into production.\n- Conducting research and experimentation with advanced ML techniques such as ensemble methods, deep learning, and time-series forecasting for credit risk prediction.\n\n### Requirements\n- 3-5 years of experience in machine learning, data science, or a related quantitative field.\n- BA/BSc/HND in a quantitative field.\n- Strong Python experience (essential).\n- Experience with machine learning frameworks - Required: scikit-learn, XGBoost, LightGBM; Nice to have: TensorFlow, PyTorch, Keras.\n- Experience with credit risk modeling is a strong plus but not mandatory.\n- Experience working with multiple data sources and data integration.\n- Familiarity with APIs - both consuming and building them.\n- Knowledge of statistical analysis and model evaluation techniques.\n- Experience with data manipulation libraries (pandas, numpy).\n- Understanding of databases - Must-have: MongoDB, PostgreSQL; Nice to have: MySQL, Redis.\n- Knowledge of data versioning, model versioning, and MLOps pipelines is a plus.\n\n### How to Apply\nInterested and qualified candidates should apply online via the Avenews application portal at which redirects to the official Avenews recruitment platform.