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Metro location, popular ML role title, and mid-to-senior experience increase applicant competition.
Core ML/AI skills are transferable but insurance risk domain knowledge increases role specificity.
Explicit years, principal-level title, appraisal and performance prerequisites, and specific technical stack make filters stringent.
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Develop and implement advanced AI, machine learning, and predictive models to create new analytics-based tools for risk management in commercial insurance.
Lead statistical analysis and data mining activities translating business problems into structured/unstructured data-driven machine learning solutions.
Serve as an internal data science expert, introducing new research approaches and supporting consulting with data-backed insights.
Minimum 12 months experience as Principal Engineer (C2) or Senior Principal Engineer.
5+ years experience in Data Science, AI research, or related fields.
Bachelor's degree in Engineering, Computer Science, Data Science, or related fields.
Hands-on expertise in Python, SQL (complex queries), and understanding of risk modeling and inferential statistics.
Experienced in building risk models using techniques like Time Series Forecasting, GLM/Regression, Clustering, Boosting, and decision trees.
Skilled at translating business problems into machine learning and statistical modeling solutions in commercial insurance or risk management context.
Capable of deploying APIs and developing statistical custom models from scratch to drive new product development and client insights.