The Opportunity
Reporting to the Manager, Enterprise Fraud, the Senior Data Specialist, is a core member of the Enterprise Fraud Analytics team. This role focuses on designing, training, and evaluating advanced machine learning models and predictive analytics to detect and prevent insurance fraud. The candidate will also support Enterprise Fraud strategic priorities and support Fraud Savings initiatives.
Operating in a highly collaborative environment, this role works alongside Business Analysts and the Special Investigations Unit (SIU). The Senior Data Specialist will leverage Google Cloud Platform (GCP) to extract predictive signals from claims, policy, and third-party data and build internal data infrastructure for fraud intelligence and model training. This role is critical in transforming complex fraud detection requirements into robust datasets and extracting predictive signals from structured and unstructured data, ensuring mathematical rigor, sound model evaluation, and explainability of AI solutions.
What to Expect
- Design, train, and iterate on machine learning models and AI solutions to identify fraudulent behavior and anomalies. Utilize a variety of supervised and unsupervised techniques (e.g., classification, clustering, anomaly detection) on structured and unstructured datasets to build robust fraud detection algorithms. Focus on Natural Language Processing (NLP), Generative AI, and Agentic workflows to extract actionable fraud indicators.
Conduct rigorous model evaluation (precision, recall, FPR) and threshold tuning. Champion Explainable AI (XAI) by utilizing methods like SHAP values or LLM reasoning traces to ensure model outputs are transparent, interpretable, and trusted by non-technical SIU investigators. - Work closely with SIU stakeholders to understand emerging fraud schemes. Conduct in-depth EDA using SQL and Python to assess data feasibility and establish baseline metrics. Design and maintain internal automations and scheduled data retrievals to improve the operational efficiency of the analytics team.
- Draft technical documentation detailing model architecture, assumptions, limitations, and evaluation metrics. Promote software engineering best practices (version control, CI/CD) and work with compliance teams to ensure AI solutions adhere to data privacy standards (PII/PHI) and algorithmic fairness guidelines.
What you Bring
- University degree in Computer Science, Data Science, Mathematics, Software Engineering, or a related quantitative discipline. (Master’s degree is an asset).
- 2-5 years of professional experience in a relevant role.
- Advanced proficiency in SQL and Python (pandas, scikit-learn) for complex data extraction, data processing, and model development.
- Broad foundational knowledge of machine learning techniques (e.g., tree-based models, regression, clustering, neural networks). Experience with GCP and Vertex AI is a strong asset.
- Experience with Natural Language Processing (NLP), text classification, and Large Language Models (LLMs) / prompt engineering.
- Strong understanding of the ML lifecycle and version control systems.
- Experience implementing Explainable AI (XAI) techniques to translate complex model decisions to business stakeholders is an asset.
- Insurance industry knowledge, Fraud Risk Management, and strong business acumen are assets.
- Strong communication (oral/written) and organizational skills. Bilingual in English/French is an asset.
Salary Range: $73,500-$123,500