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Degree in Data Science, Statistics, Mathematics, Econometrics, or a related field
Strong programming skills in Python and SQL (R is a plus)
Solid understanding of machine learning techniques and their practical trade-offs
Experience with Azure Machine Learning or similar platforms (AWS SageMaker, GCP Vertex AI)
Experience deploying models into production and maintaining them (monitoring, retraining, versioning)
Strong knowledge of experimentation and statistical methods (A/B testing, hypothesis testing)
Experience with model explainability techniques (e.g., SHAP, LIME), especially in regulated environments
Ability to translate complex analyses into clear business insights
Fluent in English
This is a role where your work directly influences key business decisions across risk, growth, and collections. As a Data Scientist, you’ll operate at the intersection of data, product, and engineering—developing scalable, production-level machine learning solutions in a modern Azure ML environment. You will take ownership of the full ML lifecycle, from problem definition to deployment and monitoring, while collaborating closely with stakeholders to turn data into insights that drive real, measurable impact.