Description

Design and optimize next-generation alerting, anomaly detection, and triage systems for fraud, security, and operational risk domains.

This role is hybrid.

Responsibilities

• Design and optimize alerting thresholds and anomaly detection logic for large-scale monitoring systems to improve detection accuracy and reduce noise.
• Analyze alert data to identify false positives and signal gaps, implementing improvements to enhance overall alert quality.
• Develop and apply machine learning models, including clustering and pattern recognition, to detect abnormal behavior across datasets.
• Enable GenAI-powered and agent-based workflows to automate alert analysis, enrichment, and triage recommendations.
• Build and maintain data pipelines in Databricks to support near real-time alerting and ensure models are explainable and audit-ready.

Required Skills

• 10+ years of experience in data science, analytics, or machine learning.
• Strong proficiency in Python and SQL for data analysis and model development.
• Hands-on experience with Databricks and large-scale data platforms.
• Solid understanding of anomaly detection techniques, threshold calibration, and signal optimization.
• Experience with GenAI concepts and agent-based architectures.
• Ability to translate complex data into clear, actionable insights and define alert performance metrics.

Preferred Skills

• Experience with Splunk, Datadog, or similar alerting platforms.
• Background in fraud, cybersecurity, or operational risk analytics within regulated environments.

Referral Bonus: ₹50,000

Key Skills

Education

Any Graduate