Data Scientist – Alerting & Triage Optimization – AI Fraud, Security and Risk Analytics
Location: Toronto, ON (Hybrid)
About the Role
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
Nice to Have
- Experience with GenAI or agentic AI workflows (e.g., automation, recommendation systems)
- Exposure to risk, compliance, or regulatory monitoring frameworks