Keivan is a data scientist with a strong background in machine learning, statistics, complex networks, and matrix analysis. He holds a Ph.D. in mathematics with expertise in combinatorial matrix theory and spectral graph theory.

He has led multiple data science sub-teams and delivered high-impact projects. His expertise includes designing, launching, and analyzing controlled online experiments at scale, reducing experimentation time, and decreasing uncertainty. He works extensively with time-series analysis, forecasting, recommender systems, and clustering algorithms, using tools like SQL and Python.

He has collaborated across disciplines with neuroscientists (fMRI, MEG, EEG data on epilepsy research and forming memory), social scientists and economists (dynamics on networks, choice theory), engineers (signal processing, data analytics, machine learning engineers, ML operations, data engineers, frontend/backend, SEO), and data scientists. He is adept at communicating complex ideas to cross-functional teams, decision-makers, and leaders and fostering a culture of experimentation within organizations.