Gene Ekster is a data analyst and alternative data specialist with nine years of experience translating unstructured, noisy datasets into investable signals for institutional investors. He builds end-to-end data products and productionizes them across quantitative, quantamental, and long/short fundamental strategies, drawing on deep expertise in text mining, KDD, machine learning, and big data systems (R, Python, Perl, SQL/NoSQL). As an educator and industry builder, he launched and teaches the world’s first accredited Alternative Data course at NYU Courant and has founded and advised multiple firms and standards bodies in the space. His background spans buy-side R&D, data vendor partnerships, and product leadership, giving him a rare combination of academic rigor and hands-on engineering for monetizing novel data sources. An often-overlooked strength: he designs the information systems that make alternative signals auditable and scalable for institutional workflows.
9 years of coding experience
20 years of employment as a software developer
Bachelor's degree, Bachelor's degree at University of California, Berkeley
Master of Business Administration - MBA, Master of Business Administration - MBA at Cornell Johnson Graduate School of Management
MBA, MBA at Cornell University - S.C. Johnson Graduate School of Management
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