Summary
Ezgi Gumusbas is a Senior Data Scientist with 6+ years of hands-on experience applying machine learning, deep learning, and statistical modeling to manufacturing and social network data, now driving process improvement at Ascend Performance Materials. Trained as a chemical engineer and sharpened through Flatiron and an MS in Applied Data Science from the University of Michigan, she combines domain knowledge in process optimization with practical ML deployment and dashboarding experience. Her background includes predictive maintenance, anomaly detection, time-series forecasting, and NLP-driven classification and recommendation systems, with a track record of translating business needs into validated, production-ready models. Beyond technical skills, she has seven years leading education and public-relations initiatives for NGOs, which honed her leadership, cross-cultural communication, and mentoring abilities. A lifelong learner and problem-solver, she’s as comfortable tuning XGBoost and ARIMA models as she is teaching advanced math to students or building small automation tools to streamline operations. An avid Lego-lover, she brings creativity and meticulousness to both data pipelines and team collaboration.
6 years of coding experience
8 years of employment as a software developer
Master of Science - MS Applied Data Science, Master of Science - MS Applied Data Science at University of Michigan
Data Science Immersive Student, Data Science Immersive Student at Flatiron School
Kabataş Erkek Lisesi
Bachelor of Science - BS Chemical Engineering, Bachelor of Science - BS Chemical Engineering at Boğaziçi University
Turkish, English, Urdu