Industry Experience

• Industry experience spans advanced data science and engineering work in cybersecurity-focused production environments, with responsibility across detection, analysis, optimization, and system-level decision support.

• Work includes the application of machine learning and statistical analysis to real-world security problems, such as intelligent bot detection, risk scoring, and large-scale exposure and asset discovery systems. This involved building data-driven models and scoring frameworks, designing analytical pipelines, and translating complex datasets into actionable security signals used in production systems.

• A significant part of this work focused on deep analysis and optimization of existing security platforms, including WAF and EASM systems. Responsibilities included identifying performance bottlenecks, analyzing rule-based and DSL-driven data extraction pipelines, classifying and weighting system rules, and improving system efficiency through SQL-based analytics, dashboards, and data-driven prioritization.

• Additional work covered vulnerability scanning and exposure analysis, including large-scale analysis and structuring of scanning templates and metadata to enable smarter scan selection, optimization, and post-scan analytics. Across all industry work, the emphasis was on turning raw security data into usable intelligence through careful system analysis and applied data science.