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    Machine Learning Engineer - Insurance Risk & Claims Analytics

    Technology
    CarShield
    Missouri, Saint Peters, 63376
    Permanent
    Competitive
    Hybrid

    Job Description

    CarShield, a leader in vehicle service contracts, seeks an AI Specialist to build data-driven solutions that enhance pricing, underwriting, and claims decisions in a fast-paced finance and insurance environment. You will develop and deploy machine learning and generative AI models using large customer, policy, and claims datasets to improve risk assessment, fraud detection, and customer experience. Partnering closely with operations, claims, and finance teams, you'll translate business needs into scalable AI tools, support regulatory and compliance requirements, and help drivers manage unexpected repair costs with confidence.

    Responsibilities

    • Design, build, and deploy AI and machine learning models to enhance underwriting, pricing, and claims workflows for vehicle protection products.

    • Analyze large datasets from customer, policy, and claims systems to identify trends, risks, and optimization opportunities.

    • Develop predictive models for risk scoring, fraud detection, and customer lifetime value to support finance and insurance decisions.

    • Collaborate with operations, claims, and finance teams to translate business requirements into scalable AI solutions.

    • Implement data pipelines, model monitoring, and performance reporting to ensure reliability and regulatory compliance.

    • Document models, assumptions, and data sources to support audits and internal governance standards.

    • Stay current on AI, ML, and generative AI tools relevant to finance and insurance and recommend new use cases.

    • Partner with IT to ensure secure, compliant deployment of models into production systems.

    Required Skills

    • Machine learning model development

    • Python or R programming

    • SQL and relational databases

    • Data preprocessing and feature engineering

    • Predictive analytics and statistical modeling

    • Cloud platforms (AWS, Azure, or GCP)

    • MLOps and model deployment

    • Fraud detection and risk modeling

    • Version control (Git)

    • Dashboarding/BI tools (Power BI, Tableau)

    Posted on October 5, 2026

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