Python Developer

Data Manipulation: You must be highly proficient in Pandas and NumPy to clean, sort, and process large historical datasets.

Machine Learning: Familiarity with Scikit-Learn is essential for building classification and regression algorithms that predict risk.

Statistical Modeling: Use packages like Statsmodels to apply Generalized Linear Models (GLMs), which are the industry standard for insurance ratemaking.

Premium Calculation: Using age, health status, and vehicle data to price policies and set premiums.

Underwriting: Assessing the statistical risk of insuring an individual or business.

Requirements:

Advanced proficiency in Python and SQL.

Familiarity with modern underwriting and actuarial platforms like Guidewire, hx Renew (hyperexponential), or Openkoda.

AWS (Lambda, S3) or Azure services, Docker, and CICD tools., >> Understanding of loss cost modeling, frequency vs. severity distributions, and base rate calculations.