Design and implement anomaly and pattern detection algorithms to identify irregular transaction behaviors, synthetic activities, and coordinated anomalies in real-time.
Develop predictive analytics models to forecast user engagement trends, platform utilization, potential churn, and high-value customer segments.
Automate manual data monitoring processes by building Python-based workflows and SQL-driven alert systems to detect and flag outliers instantly.
Conduct in-depth data forensics investigations on complex and unstructured log data to determine root causes of discrepancies or unusual system activity.
Collaborate with engineering teams to optimize data pipelines and structures (e.g., ClickHouse, AWS environments) to ensure high-performance model execution on live datasets.
Write scalable, production‑ready code and ensure model reliability, efficiency, and maintainability.