Data Scientist

Data Scientist 1+ year contract Hybrid – 2 days a week in Toronto

We are seeking a Data Scientist to design and optimize next-generation alerting and triage capabilities across fraud, security, and operational risk domains.

This role is centered on advancing alerting and automation capabilities by building data-driven systems that improve detection accuracy, reduce noise, and enable efficient, scalable triage.

You will play a key role in evolving from manual, reactive alert monitoring → proactive, AI-driven detection and triage , supporting enterprise initiatives such as real-time anomaly detection and multi-agent AI frameworks. What You’ll Do Design and optimize alerting thresholds and anomaly detection logic for large-scale monitoring systems (e.g., volume, pass rate, fail rate signals). Analyze alert data to identify false positives, missed detections, and signal gaps , and implement improvements to enhance alert quality. Develop and apply machine learning models (anomaly detection, clustering, pattern recognition) to detect abnormal behavior across datasets. Enable GenAI-powered and agent-based workflows to automate alert analysis, enrichment, and triage recommendations. Translate analyst workflows into automated, scalable solutions , reducing repetitive manual investigation effort. Build and maintain data pipelines and analytical workflows in Databricks and enterprise data platforms to support near real-time alerting. Define and track alert performance metrics (precision, noise reduction, escalation quality) to continuously improve signal effectiveness. Ensure all models, thresholds, and outputs are explainable, traceable, and audit-ready , aligned with regulatory and governance requirements. What You Bring Required Skills & Experience Strong experience in data science, analytics, or machine learning Proficiency in Python and SQL for data analysis and model development Hands-on experience with Databricks and large-scale data platforms (e.g., Rahona or equivalent) Solid understanding of: Anomaly detection techniques Threshold calibration and signal optimization Model behavior and performance evaluation Experience working with alerting systems, monitoring data, or operational metrics Ability to translate complex data into clear, actionable insights Technical Requirements Must Have: Python, SQL Databricks (or similar data platform) Machine Learning (anomaly detection, classification, clustering) Understanding of AI / GenAI concepts and agent-based architectures

Dashboards: Experience with Power BI or similar visualization tools Nice to Have: Exposure to Splunk, Datadog, TMX, BioCatch, or similar alerting platforms Experience in fraud, cybersecurity, or operational risk analytics Experience working in regulated or audit-driven environments Experience with GenAI or agentic AI workflows (e.g., automation, recommendation systems) Exposure to risk, compliance, or regulatory monitoring frameworks Why Join Us Work on cutting-edge AI-driven alerting and triage systems Direct impact on fraud detection, risk monitoring, and operational resilience Opportunity to build and scale enterprise-wide alert intelligence frameworks Collaborate with cross-functional teams across data, engineering, risk, and security Impact You’ll Make Enable proactive detection across critical customer and operational journeys Increase analyst efficiency by automating low-value, repetitive tasks Strengthen audit-ready, traceable, and scalable monitoring frameworks