Data Scientist Dallas - TX - Texas

Core Technical Skills

Programming

Languages:

Advanced proficiency in Python and SQL.

Python Libraries:
Pandas and Num Py 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 & Dev Ops: AWS (Lambda, S3) or Azure services, Docker, and CICD tools.

Version Control:
Git Git Hub for collaborative software development.

Domain-Specific Knowledge Rate making 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 (AWSAzure):
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 Roles & Responsibilities

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 Num Py 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 Num Py 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 Stats models to apply Generalized Linear Models (GLMs) which are the industry standard for insurance rate making.

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 (AWSAzure):
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.