Associate Data Scientist at Koalafi in Arlington, VA

Summary: Koalafi is a company focused on transforming the financing experience for consumers. They are seeking an Associate Data Scientist to help develop, deploy, and monitor machine learning models that influence credit outcomes and fraud mitigation. 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. (with guidance from senior team members) - Support the development and scaling of end-to-end ML pipelines, helping ensure 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, learning to link modeling decisions to business outcomes Required Qualifications: - Up to 2 years of experience (including internships, academic, or personal projects) 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 Required Skills: Machine learning model development, Python, pandas, numpy, xgboost, scikit-learn, SQL, Feature engineering, Model training, Experiment management, Production deployment, Performance monitoring, Drift detection, Data structures, Algorithms, Software engineering principles, Credit risk modeling, Fraud risk modeling, Probability, Statistics, Predictive modeling algorithms, Snowflake, Databricks, MLOps frameworks, MLflow, BentoML, CI/CD automation, Model observability and monitoring, Large language models (LLMs) Benefits: Comprehensive medical, dental, and vision coverage, 20 PTO days + 11 paid holidays, 401(k) retirement with company matching, Student Loan & Tuition Reimbursement, Commuter assistance, Parental leave (maternal + paternal), Inclusion and Associate Engagement Programs