Industry Experience

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

This 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. Responsibilities involved developing data-driven models and scoring frameworks, designing analytical pipelines, and transforming complex datasets into actionable signals used in production platforms.

A significant portion of this work focused on the analysis and optimization of existing security platforms, including Web Application Firewall (WAF) systems and External Attack Surface Management (EASM) platforms. 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 included vulnerability scanning and exposure analysis, involving large-scale structuring and analysis of scanning templates and metadata in order to enable smarter scan selection, scan optimization, and post-scan analytics.

Across all industry work, the emphasis was on applying analytical thinking and automation to translate complex system data into practical solutions and decision-support insights, enabling faster engineering decisions and continuous improvement of production systems.

Areas of Industry Work

Industry work is primarily centered around two closely related technical domains: cybersecurity systems and artificial intelligence.

The cybersecurity work focuses on large-scale security platforms, including web application protection, attack surface discovery, vulnerability scanning, and data-driven security analytics operating in real production environments.

In parallel, artificial intelligence work focuses on machine learning, deep learning, and computer vision, with particular emphasis on semantic segmentation problems. This includes segmentation tasks both in real-world visual environments and in medical imaging, where computer vision models are applied to analyze complex visual data and extract structured information.

Together, these areas reflect a broader focus on complex data-driven systems, where analytical methods, automation, and system-level engineering intersect.

Cybersecurity

Work related to security platforms, exposure analysis, vulnerability scanning systems, and large-scale security data analytics.

Cybersecurity

Artificial Intelligence

Work related to machine learning, deep learning, and computer vision, with a focus on semantic segmentation in real-world scenes and medical imaging.

Artificial Intelligence & Computer Vision

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