Senior Data Scientist

About Us ​ Migo is a cloud-based platform that enables companies to offer credit to their consumer and small business customers. Leveraging proprietary datasets, Migo builds ML algorithms to assess credit risk, and then offers credit lines to the companies’ customers. This credit line can be used to make purchases from a merchant or to withdraw cash without the need for point-of-sale hardware or plastic cards. Because of our proprietary data and innovative technical solutions, Migo is able to extend credit to underbanked customers who credit bureaus do not typically cover ​ Job Description This is a remote position. As a Senior Data Scientist at Migo, you will play a key role in optimising our machine learning-driven decision systems, from monitoring and retraining existing models to developing new ones that power automated lending decisions. You will analyse performance data to refine features, select optimal models for different customer segments, and integrate new data sources into our modelling frameworks. Your work will combine statistical rigour with practical problem-solving, leveraging causal inference concepts, A/B testing, and robust ETL pipelines to improve model accuracy and stability. Working closely with engineering and product teams, you will ensure our decision-making systems remain adaptable, data-driven, and effective as we scale into new markets and serve a growing customer base. Responsibilities Analyze business data to assess performance and identify areas for improvement Monitor, retrain, and iteratively improve machine learning models Add and remove features based on performance analysis Select optimal models for different customer segments using established metrics Integrate new data sources into existing modeling frameworks Analyze new data to develop rule-based and heuristic approaches Monitor and develop ETL and feature engineering pipelines Develop new ML models for automated decision-making Requirements You are a good fit if you have: An MS in Machine Learning, Data Science, Economics, or (Applied) Statistics, or equivalent experience Experience with ML lifecycle and statistical modeling Knowledge of ML concepts such as model drift and data leakage Experience developing new machine learning models Familiarity with basics of causal inference Experience with Python and object-oriented programming Experience in data pipelines and ETL processes Experience with A/B testing Full proficiency in SQL