Top AI & Machine Learning Jobs in Seattle, WA

Lead end-to-end predictive analytics initiatives, translating business objectives into data science solutions. Develop and deploy machine learning, deep learning, statistical modeling, and computer vision models for insurance use cases. Manage experimentation, model validation, monitoring, reproducible workflows, cloud deployment, production pipelines, and incident troubleshooting. Partner with stakeholders, communicate findings to senior audiences, document methodologies, and provide technical guidance through code reviews and best practices.

Leads Specialty Lines data science for insurance pricing, including predictive modeling and rating program design for Condo and RV products. Represents data science requirements on a major platform transformation, researches modeling techniques and data sources, communicates findings to executives, and manages, coaches, and develops a team of 3–5 data scientists. Requires strong insurance, statistical modeling, technical, communication, and cross-functional leadership expertise.

Own model observability across auto claims models, building monitoring infrastructure, dashboards, alerts, health metrics, and remediation processes for traditional ML and GenAI systems. Detect drift, performance degradation, data-quality issues, and anomalous behavior; partner with data scientists, engineers, business stakeholders, and governance teams. Produce audit-ready reporting, evaluate model outcomes against business KPIs, support MLOps/LLMOps integration, and communicate model health findings to technical and non-technical audiences.

Lead the Generative AI and ML portfolio for Knowledge & Development Solutions: identify and prioritize AI/ML use cases, design/build/evaluate models and RAG/semantic search solutions, partner with stakeholders to productionize and measure impact, and provide technical mentorship and thought leadership to improve knowledge discovery, learning outcomes, and employee enablement.

Lead AI program management for Small Commercial Product: own the AI investment roadmap, prioritize and track AI initiatives, drive cross-functional execution and adoption, coordinate risk/compliance, partner with Data Science and Technology, and monitor AI trends to deliver measurable business outcomes.

Owner of model observability for APD models: build monitoring pipelines, dashboards, and alerts for ML and GenAI systems; detect and triage drift, performance decay, and anomalies; integrate monitoring into deployment CI/CD; produce audit-ready governance evidence; analyze production outcomes versus business KPIs; communicate findings to technical and business stakeholders.