Associate Data Scientist

About the role

Koalafi is a company that provides flexible lease-to-own financing to non-prime consumers, helping retailers increase sales and strengthen customer loyalty. As a Data Scientist, you will contribute to the development and maintenance of machine learning models that influence credit outcomes, fraud mitigation, and the financial performance of the company.

Responsibilities

• Help build, deploy, and maintain production-grade credit and fraud models that support our real-time decisioning platform and portfolio profitability.
• Contribute across the MLOps lifecycle: Feature engineering, model training, experiment management, production deployment, performance monitoring, and drift detection.
• Support the development and scaling of end-to-end ML pipelines, ensuring reliability, reproducibility, and integration with core decisioning services.
• Assist in building model monitoring that enables tracing, profiling, explainability, and root-cause analysis for production incidents or model degradation.
• Partner with risk and engineering teammates to improve credit policy and strengthen fraud defenses in response to customer behavior and macroeconomic trends.
• Contribute to the continuous improvement of existing models by exploring new data sources, techniques, and validation processes.
• Communicate model logic and insights clearly, linking modeling decisions to business outcomes.

Qualifications

• Up to 2 years of experience building and deploying machine learning models, with familiarity in the modeling lifecycle from feature engineering to validation.
• Up to 2 years of experience writing Python, including core data science libraries like pandas, numpy, xgboost, and scikit-learn.
• Working knowledge of SQL for querying, transforming, and analyzing datasets.
• Understanding of data structures, algorithms, and software engineering principles, with a willingness to apply them to build robust, scalable solutions.
• Bachelor’s degree in a quantitative or STEM field (e.g., Statistics, Mathematics, Computer Science, Engineering), with strong analytical and problem-solving skills.
• Preferred qualifications include exposure to credit or fraud risk modeling, strong analytical foundation with a Master’s in a quantitative or STEM field, and familiarity with modern ML infrastructure and tooling.