workable
Insurance Underwriting AI Expert
Weekday
Remote, USA
Part-time
Junior · 2–8 years experience
Remote
Discovered 3 days ago
pythonsql
Key Skills for This Role
pythonsql
Full Job Posting
Key Responsibilities:
- Design and implement AI/ML models to support risk assessment and selection, improving accuracy in evaluating applicant profiles, exposures, and potential losses.
- Translate underwriting guidelines and business rules into data models and algorithms that automate and augment decision-making.
- Develop and refine policy pricing and rating models using statistical and machine learning techniques, ensuring alignment with market competitiveness and profitability goals.
- Analyze historical data and external datasets to identify risk patterns, trends, and anomalies that inform underwriting strategies.
- Support appetite evaluation and guideline application by building intelligent systems that dynamically match risks to underwriting appetite and highlight exceptions.
- Collaborate with cross-functional stakeholders to integrate AI models into underwriting platforms and workflows.
- Continuously monitor model performance, validate outputs, and recalibrate algorithms to ensure compliance, fairness, and accuracy.
- Contribute to the development of underwriting dashboards, reporting tools, and decision-support systems.
- Stay updated on regulatory requirements and ensure all AI-driven underwriting practices adhere to compliance standards.
- Required Skills & Qualifications:
- Bachelor’s or Master’s degree in Insurance, Actuarial Science, Data Science, Statistics, or a related field.
- 2–8 years of experience in insurance underwriting, analytics, or AI/ML applications within the insurance domain.
- Strong understanding of underwriting principles, including risk evaluation, pricing strategies, and policy structures.
- Hands-on experience with machine learning techniques such as regression, classification, clustering, and predictive modeling.
- Proficiency in tools such as Python, R, SQL, and data visualization platforms.
- Experience working with large datasets, data preprocessing, and feature engineering.
- Familiarity with underwriting systems, rating engines, and insurance data structures is highly preferred.
- Strong analytical thinking, problem-solving abilities, and attention to detail.
Preferred Qualifications:
- Exposure to commercial or personal lines underwriting.
- Experience with AI model deployment and MLOps practices.
- Knowledge of regulatory frameworks impacting underwriting and pricing.
- Familiarity with underwriting systems, rating engines, and insurance data structures is highly preferred.
Qualifications
- Design and implement AI/ML models to support risk assessment and selection, improving accuracy in evaluating applicant profiles, exposures, and potential losses.
- Translate underwriting guidelines and business rules into data models and algorithms that automate and augment decision-making.
- Develop and refine policy pricing and rating models using statistical and machine learning techniques, ensuring alignment with market competitiveness and profitability goals.
- Analyze historical data and external datasets to identify risk patterns, trends, and anomalies that inform underwriting strategies.
- Support appetite evaluation and guideline application by building intelligent systems that dynamically match risks to underwriting appetite and highlight exceptions.
- Collaborate with cross-functional stakeholders to integrate AI models into underwriting platforms and workflows.
- Continuously monitor model performance, validate outputs, and recalibrate algorithms to ensure compliance, fairness, and accuracy.
- Contribute to the development of underwriting dashboards, reporting tools, and decision-support systems.
- Stay updated on regulatory requirements and ensure all AI-driven underwriting practices adhere to compliance standards.
- Bachelor’s or Master’s degree in Insurance, Actuarial Science, Data Science, Statistics, or a related field.
- 2–8 years of experience in insurance underwriting, analytics, or AI/ML applications within the insurance domain.
- Strong understanding of underwriting principles, including risk evaluation, pricing strategies, and policy structures.
- Hands-on experience with machine learning techniques such as regression, classification, clustering, and predictive modeling.
- Proficiency in tools such as Python, R, SQL, and data visualization platforms.
- Experience working with large datasets, data preprocessing, and feature engineering.
- Strong analytical thinking, problem-solving abilities, and attention to detail.
About Weekday
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