Data Scientist – Alerting & Triage Optimization
Location: Toronto, ON (Hybrid)
Job Overview
We are seeking an experienced Data Scientist to design and optimize next-generation alerting and triage capabilities across fraud, security, and operational risk domains.
The role focuses on building data-driven solutions that improve detection accuracy, reduce alert noise, and enable efficient, scalable triage. You will help transform manual, reactive alert monitoring into proactive, AI-driven detection and triage, including real-time anomaly detection and multi-agent AI frameworks.
Key Responsibilities
- Design and optimize alerting thresholds and anomaly detection logic for large-scale monitoring systems.
- Analyze alert data to identify false positives, missed detections, and signal gaps and improve alert quality.
- Develop and apply machine learning models for anomaly detection, clustering, and pattern recognition.
- Enable GenAI-powered and agent-based workflows for automated alert analysis, enrichment, and triage recommendations.
- Translate analyst workflows into automated and scalable solutions to reduce manual investigation.
- Build and maintain data pipelines and analytical workflows in Databricks and enterprise data platforms.
- Define and track alert performance metrics such as precision, noise reduction, and escalation quality.
- Ensure models, thresholds, and outputs are explainable, traceable, and audit-ready.
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 or similar large-scale data platforms.
- Strong understanding of:
- Anomaly Detection
- Threshold Calibration
- Signal Optimization
- Model Performance Evaluation
- Experience working with alerting systems, monitoring data, or operational metrics.
- Ability to translate complex data into clear, actionable insights.