Reporting to the Manager, Enterprise Fraud, the Senior Data Specialist is a core member of the Enterprise Fraud Analytics team. This hybrid role bridges the gap between data engineering and data science, focusing on architecting scalable data pipelines and designing advanced AI/ML models 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 heavily leverage Google Cloud Platform (GCP), BigQuery, Vertex AI, and Gemini. This role is critical in transforming complex fraud detection requirements into robust datasets and extracting predictive signals from unstructured text and claims data, ensuring both architectural scalability and mathematical rigor.

What to Expect

Data Architecture & Pipeline Engineering
Design, build, and maintain highly scalable ELT/ETL data pipelines using modern cloud infrastructure (GCP, BigQuery, GCS). Architect processes to ingest, transform, and integrate large volumes of structured claims data and unstructured third-party data to build the foundational datasets required for advanced fraud analytics.
Algorithm Design & AI Model Development
Design, train, and iterate on predictive machine learning models and AI solutions. Focus heavily on Natural Language Processing (NLP) and Generative AI (e.g., prompt engineering with Gemini) to extract actionable fraud indicators from unstructured SIU data (case notes, medical records) and structured policy data.
Model Evaluation & Explainability
Conduct rigorous offline model evaluation (precision, recall, slice analysis). 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.
Exploratory Data Analysis (EDA) & Automation
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.
Governance, Compliance & Best Practices
Draft comprehensive technical documentation detailing pipeline lineage, model architecture, 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, Software Engineering, or a related quantitative discipline. (Master’s degree is an asset).
3-5 years of professional experience in a relevant role.
Advanced proficiency in SQL and Python (pandas, scikit-learn) for complex data extraction, data processing, and/or model development.
Familiarity in modern cloud data warehousing and ML platforms, with Google Cloud Platform (BigQuery, Vertex AI) and orchestration tools (e.g., Airflow, dbt) strongly preferred.
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.
Insurance industry knowledge, Fraud Risk Management, and strong business acumen are assets.
Strong communication skills (oral/written). Bilingual in English/French is an asset.

Salary Range: $73,500-$123,500