Risk Analytics Expert

CanadaRemoteFull-timemid

AnalyticsData & AI

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Quick Summary

Overview

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Risk Analytics Expert based in Canada.

Technical Tools

AnalyticsData & AI

We are looking for a data-driven insurance analytics expert to help insurers better understand, evaluate, and manage risks associated with natural catastrophes and property characteristics. In this remote role, you will combine actuarial knowledge, advanced analytics, and AI-powered insights to solve complex challenges in property and casualty insurance. You will work closely with prospective and existing clients, guiding them through product demonstrations, model validation, pilot projects, and implementation strategies. By translating complex analytical results into clear, actionable recommendations, you will support informed underwriting and pricing decisions. You will also collaborate with cross-functional teams to improve products, streamline workflows, and help shape innovative solutions for a rapidly evolving insurance landscape.

Lead product demonstrations and technical presentations, helping sales and customer success teams communicate the value and capabilities of advanced risk analytics solutions.
Advise prospective clients on regulatory filing requirements and implementation strategies, coordinating with actuarial consultants and maintaining strong working relationships.
Design and manage pilot projects and proofs of concept from start to finish, including sampling strategies, integration of carrier loss data, model performance analysis, lift assessments, and loss ratio evaluations.
Present analytical findings and practical recommendations to actuarial, underwriting, and other business leaders, making complex technical concepts accessible to non-technical audiences.
Develop deep expertise in risk analytics solutions, including their competitive advantages over traditional modeling approaches and alternative market offerings.
Manage client-facing projects from initial discovery through delivery, coordinating cross-functional teams to ensure accuracy, timeliness, and high-quality outcomes.
Lead discovery sessions to identify client challenges, understand business requirements, and connect customer use cases to appropriate analytical solutions.
Share client insights and product feedback with internal product teams to support ongoing innovation and product development.
Build and refine AI-assisted workflows that accelerate research, analysis, and deliverable creation, developing reusable processes that improve team efficiency.
Support vendor evaluations and help clients understand how third-party data and analytical models can be incorporated into insurance pricing and underwriting processes.

Requirements

~2 min read

Bachelor’s degree in a quantitative discipline, such as actuarial science, statistics, economics, engineering, data science, or a related field.
At least five years of experience in property and casualty insurance analytics, actuarial functions, underwriting, insurance product management, or a vendor role serving insurance carriers.
Hands-on experience validating analytical models using techniques such as lift charts, Gini coefficients, and loss ratio relativities, with the ability to explain results and implications to non-technical stakeholders.
Proficiency in SQL and Python for querying, processing, and analyzing large datasets, with experience using BigQuery or similar platforms.
Regular use of AI tools such as ChatGPT, Claude, or Copilot to support research, analysis, and writing, combined with sound judgment in validating AI-generated outputs.
Working knowledge of insurance rate filings, classification plans, and the integration of third-party data into pricing and underwriting processes.
Strong communication, presentation, and data storytelling skills, with the ability to engage audiences ranging from technical analysts to executive leadership.
Proven ability to own client deliverables, manage competing priorities, and consistently meet deadlines.
Preferred qualifications: Experience developing AI agents or automated workflows, familiarity with insurance AI regulations and unfair discrimination testing, and knowledge of catastrophe or climate modeling platforms.
Additional advantages include geospatial analytics experience with tools such as ArcGIS, QGIS, GeoPandas, or H3; sales engineering or SaaS customer-facing experience; participation in vendor evaluations; and familiarity with business intelligence tools such as Looker, Power BI, or Tableau.