Actuarial Analyst II

TD•Montreal, QC

1d•CA$66,800 - CA$94,300•Onsite

About The Position

As part of TD Insurance’s Data, Analytics & Insights (DAI) team, the Fraud Analytics & Insights Analyst plays a critical role in transforming data into actionable intelligence that strengthens fraud detection, prevention, and business decision-making. This is not a traditional reporting role. You will partner closely with Fraud Operations, Policy and Claims Investigations teams to build scalable analytics solutions, automate insights, and influence fraud strategies. Your work will directly contribute to reducing fraud exposure, improving detection accuracy, and enabling faster, smarter decisions. Join a team where data doesn’t just inform — it actively disrupts fraud.

Requirements

Bachelor's or Master’s degree in Actuarial science, Mathematics, Statistics, Business Intelligence, Data Science, Computer Science, Finance, or a related field
2-7 years of experience
Expertise in analytics, data analysis, and statistical analysis
Experience in the general insurance industry
Experience with fraud detection, anomaly identification, or risk analytics frameworks
Bilingual in English and French (required)
Strong coding skills in SQL and Python
Experience with data visualization tools (e.g., Power BI, Tableau, or Shiny/Posit)
Excellent analytical skills and attention to detail, especially for quality control and data validation
Strong communication skills, with ability to explain complex concepts to both technical and non-technical audiences
Skilled in critical thinking, creative problem-solving, and time management
Comfortable working independently and collaboratively in a dynamic environment

Responsibilities

Design and build automated dashboards and pipelines to monitor fraud performance end-to-end (intake → detection → investigation → outcome)
Develop self-serve analytics tools to empower Fraud Operations and business partners
Contribute to the automation of fraud reporting and workflows (e.g., reducing manual processes, improving data timeliness)
Perform deep-dive exploratory analyses to identify fraud patterns, emerging risks, and operational gaps.
Translate complex findings into clear, actionable recommendations that influence fraud strategy and business rules
Evaluate the performance and impact of fraud initiatives (e.g., detection models, business rules, awareness tactics)
Act as a thought partner to Fraud and Underwriting operations translating business problems into analytical solutions.
Act as an AI enabler, embedding artificial intelligence into day-to-day fraud analytics processes to modernize workflows, automate insights, and enhance detection capabilities and decision-making
Ensure data quality and integrity through validation, documentation, and continuous improvement of data processes
Advance data literacy across the team, promoting best practices in business intelligence and data storytelling.
Mentor and support team growth, sharing technical knowledge and business insight to uplift team capability.