Position Title: Data Scientist – Alerting & Triage Optimization

Department: Data Science and Advanced Analytics

Location: Toronto, ON (Hybrid)

Type: Contract (6 Months)

Role Overview We are seeking a highly technical Data Scientist to design and optimize next-generation alerting and triage capabilities across fraud, security, and operational risk domains. You will play a key role in evolving our systems from manual, reactive alert monitoring to proactive, AI-driven detection and triage. By engineering robust risk signals and applying strong software developer experience, you will support enterprise initiatives such as real-time anomaly detection and multi-agent AI frameworks. You will play a pivotal role in the "future development" of our data products, building scalable systems that improve detection accuracy, reduce noise, and enable efficient, scalable triage.

Key Responsibilities

• Design, develop, and optimize alerting thresholds and anomaly detection logic for large-scale monitoring systems, including volume, pass rate, and 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, such as anomaly detection, clustering, and pattern recognition, to detect abnormal behavior across datasets.
• Apply strong software engineering principles to write clean code, translating analyst workflows into automated, scalable solutions to reduce repetitive manual investigation effort.
• Enable GenAI-powered and agent-based workflows to automate alert analysis, enrichment, and triage recommendations.
• 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, such as precision and noise reduction, while ensuring all models and outputs are explainable, traceable, and audit-ready to align with regulatory
• requirements.

Qualifications

• Strong experience in data science, analytics, or machine learning with deep expertise in mathematical modeling.
• Proficiency in Python and SQL for data analysis and model development.
• Hands-on experience with Databricks and large-scale data platforms, such as Rahona or equivalent.
• Solid understanding of anomaly detection techniques, threshold calibration, and signal optimization.
• Demonstrated software developer experience with a track record of deploying robust models into production environments.
• 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; and experience with risk, compliance,
• or regulatory monitoring frameworks.

Must-Have Qualifications

• Risk Signal & Alerting Engineering: A proven track record of conceptualizing and deploying risk signals from complex data sources, coupled with experience working with alerting systems and operational metrics.
• Production Developer & Data Platform Experience: Strong coding and software engineering capabilities, specifically utilizing Python, SQL, and Databricks to write scalable, production-grade code.
• Mathematical & Machine Learning Modeling: Deep theoretical understanding and practical application of complex mathematical models, including anomaly detection, classification, and clustering.
• Future Development & GenAI Mindset: Understanding of AI, GenAI concepts, and agent-based architectures to build predictive solutions designed for long-term enterprise growth and automation.