RLI is looking to apply AI/ML across Personal Umbrella Policy (PUP) and E&S Property lines of business to improve underwriting precision, claims operations, portfolio risk management, and product innovation
Requirements
- 3–5 years of hands-on experience in applied data science or machine learning roles
- Proficiency in Python (e.g., scikit-learn, XGBoost, pandas) and SQL
- Experience developing and deploying models in Snowflake using Snowpark, Cortex, or similar cloud-native tools
- Strong understanding of supervised learning, feature engineering, and model performance evaluation
- Background in insurance, risk modeling, or financial services
- Experience working in environments with governed data platforms and production ML workflows
- Exposure to MLOps practices, model versioning, and drift monitoring
Responsibilities
- Design, create, document and maintain user-friendly and purpose-built data products for use by models and non-technical end users
- Develop, train, and deploy models using Snowflake Cortex, Snowpark, and integrated Python execution
- Own the end-to-end model lifecycle: experimentation, feature engineering, validation, production deployment, and monitoring
- Work closely with our underwriting, claim, actuarial, and analyst teams to translate business needs into data science solutions
- Partner with data engineering to ensure the availability and readiness of Snowflake-based datasets for modeling and inference
- Contribute to the development of model governance, reusability standards, and operational monitoring frameworks
- Stay current on Snowflake’s evolving capabilities to ensure model development aligns with platform best practices
Other
- In-office requirement of 3 days minimum in our Peoria or Chicago office
- Excellent problem-solving, communication, and documentation skills
- Proven ability to work cross-functionally with both technical and non-technical stakeholders
- Paid time off (PTO) and holidays
- Parental and family care leave
