Summary
Masoud Erfani is a Senior DevSecOps and Detection Engineering professional based in Calgary with 10 years of software experience and over 6 years coding in Python, Java, and MATLAB. He blends hands-on security engineering at PwC Canada—designing MITRE-aligned SIEM use cases and leading detection teams—with academic research in machine learning and anomaly detection from his MSc. Comfortable across Splunk, Microsoft Sentinel, XSOAR, ELK and MISP, he automates investigations with Python and builds SOAR playbooks and Logstash filters to streamline incident response. His research background includes practical deep-learning and one-class ensemble approaches for imbalanced fraud detection and streaming anomaly detectors applied to real-world IoT and user-behavior datasets. A practiced mentor and instructor, he onboards new joiners and has taught data structures, software security, and AI courses, reflecting a talent for translating research into operational security. Beyond typical SecOps skills, he brings a pattern-recognition mindset from computer vision and time-series work that improves detection coverage and reduces false positives.
10 years of coding experience
7 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at University of New Brunswick
Bachelor's degree Computer Software Engineering, Bachelor's degree Computer Software Engineering at Ferdowsi University of Mashhad
high school Mathematics, high school Mathematics at National Organization for Development of Exceptional Talents (Sampad)
English, Persian