Description

• Rating Engine Development: Write and maintain backend code that ingests risk attributes and calculates accurate policy premiums, discounts, and surcharges. • Actuarial Translation: Collaborate with actuaries to translate manual rate manuals (like those filed in SERFF) and statistical models into executable, production-grade code. • Data Pipelines & ETL: Build and manage data pipelines using libraries like Pandas and NumPy to process historical claims and policy data. • Predictive Pricing Modeling: Develop machine learning algorithms (e.g., using Scikit-Learn) to evaluate risk loss and optimize pricing models. • Compliance & Auditing: Ensure rating logic complies with state insurance regulations by building logging and auditing mechanisms directly into the code. Essential Skills: • 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. • Version Control: Standard team workflows require the use of Git to manage code updates and track model versions securely. • remium 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. • SQL (Structured Query Language): The coding language used to pull raw data from massive insurance databases. • Predictive Modeling (Machine Learning): Using code to guess which drivers will cost the company the most money. • Data Visualization: Using Python packages like Matplotlib or Seaborn to turn complex pricing data into easy-to-read charts for business leaders. • Cloud Computing (AWS/Azure): Running massive pricing models on remote computers so your laptop does not crash. • Regulatory Compliance: Understanding state laws and rules to ensure your Python pricing models do not violate fair housing or discrimination rules

Core Technical Skills • Programming Languages: Advanced proficiency in Python and SQL. • Python Libraries: Pandas and NumPy for data manipulation; Scikit-Learn for predictive modeling. • Insurance Platforms: Familiarity with modern underwriting and actuarial platforms like Guidewire, hx Renew (hyperexponential), or Openkoda. • Cloud & DevOps: AWS (Lambda, S3) or Azure services, Docker, and CI/CD tools. • Version Control: Git / GitHub for collaborative software development. • Domain-Specific Knowledge Ratemaking Fundamentals: Understanding of loss cost modeling, frequency vs. severity distributions, and base rate calculations. • Underwriting Rules: Knowledge of how Motor Vehicle Records (MVR), garaging territories, and vehicle safety features impact risk tiering. • Telematics: Experience parsing and utilizing data from usage-based insurance (UBI) trackers to adjust rates based on driving behavior. • SQL (Structured Query Language): The coding language used to pull raw data from massive insurance databases. • Predictive Modeling (Machine Learning): Using code to guess which drivers will cost the company the most money. • Data Visualization: Using Python packages like Matplotlib or Seaborn to turn complex pricing data into easy-to-read charts for business leaders. • Cloud Computing (AWS/Azure): Running massive pricing models on remote computers so your laptop does not crash. • Regulatory Compliance: Understanding state laws and rules to ensure your Python pricing models do not violate fair housing or discrimination rules

Key Skills

Education

Bachelor's degree