Data Scientist

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., * 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.

• 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, * 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