Actuarial background required; credentialed actuary (ACAS/FCAS or equivalent) strongly preferred.
PhD (preferred) or Master’s in a quantitative field such as Actuarial Science, Statistics, Mathematics, Engineering, Computer Science, or related quantitative field.
Minimum of years of experience across actuarial, advanced analytics, or data science roles, including significant hands-on model development experience.
Hands-on experience in any of these items: pricing, rate indications, product optimization, actuarial forecasting, loss modeling, profitability analysis, underwriting, risk selection and portfolio management.
Years of leadership experience managing high-impact data science or cross-functional teams (preferred), with continued personal technical contribution.
Proven track record in the insurance industry, particularly within Product, Pricing, Underwriting, or Risk domains.
Advanced proficiency in Python and SQL for data manipulation and model development, with recent, ongoing hands-on usage.
Deep expertise in actuarial methods, machine learning, statistics, and MLOps practices.
Hands-on experience building, deploying, and maintaining scalable analytics models on large insurance datasets.
Skilled in project scoping, planning, and delivery of robust, business-aligned AI solutions.
Experience with GenAI, NLP, and advanced modeling techniques across structured and unstructured data.
Strong grasp of model governance, responsible AI, and regulatory compliance.
Excellent communicator with the ability to translate complex technical insights for diverse audiences.
Strong influencing, negotiation, and conflict resolution skills.