- Design and execute data science experiments, including causal analysis, A/B tests, and offline evaluation.
- Develop, evaluate, and iterate on predictive models for credit/risk scoring, revenue forecasting, and policy performance.
- Own model performance and monitoring: define success metrics, investigate drift, and drive improvements to data quality and feature reliability.
- Partner with Product Engineering to productionize models and analytics with emphasis on reliability, reproducibility, and maintainability.
- Perform exploratory data analysis, feature engineering, and robust validation on real-world, messy data.
- Communicate insights and recommendations clearly to technical and non-technical stakeholders through documentation and presentations.
- Improve analytical standards, code review practices, and documentation to raise technical quality.
- Mentor and support team members through pairing, feedback, and sharing best practices.
PythonSQLMachine Learning+3 more