Associate Data Scientist

About the role

Koalafi is a company that offers 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, deployment, and monitoring of machine learning models that enhance 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 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.

Requirements

• Up to 2 years of experience building and deploying machine learning models, with familiarity with the modeling lifecycle from feature engineering to validation.
• Up to 2 years of experience writing Python, including core data science libraries such as 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 an eagerness 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

• Exposure to credit or fraud risk modeling through coursework, internships, or projects.
• Strong analytical foundation, ideally with a Master’s in a quantitative or STEM field, and an understanding of probability, statistics, and predictive modeling algorithms (e.g., Boosting, Random Forests, Decision Trees, Bayesian models).
• Exposure to data and compute platforms such as Snowflake and Databricks.
• Interest in financial services, or experience in fast-moving, high-growth environments such as startups.
• Familiarity with modern ML infrastructure and tooling, including MLOps frameworks (e.g., MLflow, BentoML), CI/CD automation, and model observability and monitoring.
• Familiarity with large language models (LLMs) and their deployment.