Kiarash Golzadeh is a data engineer with 8 years of experience who builds production-ready ML data pipelines and recommender systems, currently contributing to lookalike modeling at StackAdapt. He holds a Master's in Computer Science from the University of Waterloo and a top-ranked Bachelor's in Computer Engineering from Sharif University, blending strong academic foundations with applied engineering. His background spans research (Max Planck Institute), fintech-grade pipeline work for Tehran Stock Market, and R&D roles, showing fluency from algorithms and optimization to full-stack deployment (Docker, Nginx). A seasoned TA across multiple CS courses, he communicates complex concepts clearly and has taught algorithms and programming at both university and olympiad levels. Comfortable with Python, data stacks and web services, he brings a pragmatic research-to-production mindset that accelerates ML model delivery. Notably, he has implemented mixed-integer and semi-definite programming tools in research contexts, signaling depth in discrete optimization beyond typical data-engineer skillsets.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Computer Engineering, 18.19/20, Bachelor's degree, Computer Engineering, 18.19/20 at Sharif University of Technology
High School, Mathematics, 19.90/20, High School, Mathematics, 19.90/20 at Allameh Helli 3
Bronze medal, Olympiad in Informatics, Bronze medal, Olympiad in Informatics at Young Scholars Club
Master's degree, Computer Science, Master's degree, Computer Science at University of Waterloo
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