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.

Data Scientist – Alerting & Triage Optimization

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