Data Scientist - P&C Underwriting

RESPONSIBILITIES:
Kforce has a client in San Antonio, TX that is seeking experienced Data Scientists to join a high-impact team within P&C Data Science - Actuary & Analytics (A&A).

Summary:
This team focuses on building and automating predictive models that drive risk selection, coverage optimization, and fraud detection. The work is highly technical and compliance-driven, combining data engineering, machine learning, and risk management. These roles are critical to achieving our mission of creating models that determine the best insurance decisions and ensuring data quality across all risk-related processes.

Key Responsibilities:
* Design, develop, and automate predictive models for risk selection, coverage optimization, and fraud detection
* Perform monthly, quarterly, and annual Model Risk and Compliance assessments to ensure regulatory and internal standards are met
* Collaborate with cross-functional teams to improve data quality and integrate models into production environments
* Implement machine learning techniques (Cisco Modeling preferred) for model development and automation
* Monitor and maintain models to ensure performance and accuracy over time
* Support future initiatives in advanced model development and geo-spatial modeling
* Develop and implement data-driven solutions to improve underwriting performance
* Experiment with various data sources and statistical modeling techniques
* Automate statistical model monitoring and data analysis
* Document and publish model monitoring reports utilizing markdown or similar software
* Version, publish, and maintain model monitoring code utilizing SQL and python
* Prototype, test, and deploy machine learning models into production
* Collaborate with stakeholders across the organization to identify and address key business challenges
* Work with ambiguous data to derive meaningful insights and create actionable recommendations

REQUIREMENTS:
* Master's degree in Computer Science, Data Science, Statistics, or a related field; PhD or additional experience is often preferred
* 5+ years of experience as a Data Scientist with a strong background in data engineering and model development
* 2-5 years of experience with Risk Modeling and Geo-Spatial Modeling
* Demonstrated ability to solve complex data problems with limited initial information
* Experience working effectively in a collaborative environment with diverse stakeholders
* Exceptional attention to detail and commitment to data quality
* Proficiency in: Python, SQL (MySQL, PostgreSQL, Apache Hive, BigQuery SQL), and Snowflake

Hands-on experience with:
* Machine Learning (Cisco Modeling preferred, open to general ML techniques): Proven experience building and deploying machine learning models in a production environment
* Model Development & Automation
* Fraud Models or Fraud Indicators

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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