Koalafi is seeking to improve its portfolio's profitability by developing, deploying, and monitoring machine learning models that sit at the core of its decisioning ecosystem, specifically for credit and fraud risk models.
Requirements
- 5+ years of hands-on experience building and deploying machine learning models, with a strong grasp of the end-to-end modeling lifecycle from feature engineering to validation and productionization
- 5+ years of professional experience writing performant, maintainable Python code in a collaborative production environment, leveraging core data science libraries like pandas, numpy, xgboost, and scikit-learn
- 2+ Years of experience working on Credit or Fraud risk models
- Proficient in SQL for querying, transforming, and analyzing large datasets, and comfortable working across relational databases and cloud-based data platforms
- Strong understanding of data structures, algorithms, and software engineering principles, and apply them to build robust and scalable data solutions
- Advanced technical and analytical background, ideally with a Master’s or PhD in a quantitative or STEM field, and a strong 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
Responsibilities
- Build, deploy, and maintain production-grade credit and fraud models that are foundational to our real-time decisioning platform and essential to portfolio profitability
- Own the full MLOps lifecycle—from feature engineering, model training, and experiment management to production deployment, performance monitoring, drift detection, and continuous optimization
- Architect and scale end-to-end ML pipelines, ensuring reliability, reproducibility, and seamless integration with core decisioning services
- Design robust model monitoring frameworks that enable tracing, profiling, explainability, and rapid root-cause analysis for production incidents or model degradation
- Partner with data science, risk, and engineering leaders to shape modeling strategy, improve credit policy, and strengthen fraud defenses in response to customer behavior and macroeconomic trends
- Drive continuous improvement of existing models, incorporating new data sources, advanced techniques, and rigorous validation processes
- Communicate complex model logic and insights to non-technical stakeholders, clearly linking modeling decisions to business outcomes and strategic priorities
Other
- Bachelor’s degree in a quantitative or STEM field (e.g., Statistics, Mathematics, Computer Science, Engineering) and demonstrate strong analytical and problem-solving skills in your work
- Comprehensive medical, dental, and vision coverage
- 20 PTO days + 11 paid holidays
- 401(k) retirement with company matching
- Student Loan & Tuition Reimbursement
