Soheil Hajian is an applied scientist and experienced ML engineer with nine years of experience turning mathematical models into production-ready data products. He blends a PhD-level background in computational mathematics with hands-on engineering—building low-latency fraud detection systems and scalable ML pipelines using Python, C++, Fortran90 and cloud/Spark tooling. At Zalando and previous roles he has delivered real-time solutions (sub-50ms responses) and contributed both to architecture and core algorithm design. His research pedigree includes topology optimization, Bayesian inverse problems and parallel numerical methods, reflecting a rare mix of theoretical depth and production pragmatism. He also serves as an external editor for zbMATH, showing continued engagement with the research community beyond industry work. Based in Berlin, he favors elegant, performance-aware implementations that bridge novel algorithms and robust engineering.
9 years of coding experience
6 years of employment as a software developer
Master of Science (M.Sc.), Mathematical modeling in Engineering, 110/110, Master of Science (M.Sc.), Mathematical modeling in Engineering, 110/110 at Universitat Autònoma de Barcelona
Doctor of Philosophy (Ph.D.), Mathematics, Doctor of Philosophy (Ph.D.), Mathematics at University of Geneva
Bachelor of Applied Science (BASc), Physics, 17/20, Bachelor of Applied Science (BASc), Physics, 17/20 at Shahid Beheshti University
Contributions:20 commits, 12 pushes, 1 branch in 2 months
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