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Daniel Nissani is a machine learning engineer at Arthur AI. Daniel was previously a machine learning researcher at Gretel.ai from July 2021 to April 2022, where they worked on user-centered research, primarily on natural language processing, ethics, and anything else that would help improve the company's synthetics product. Daniel also ran full end-to-end experiments to validate hypotheses and build out new models, and wrote production level code for seamless hand off to engineers. At Cigna, from May 2020 to June 2021, they were a data scientist experimenting with new data sources to upgrade performance of Cigna's renewal risk score models, primarily through leveraging NLP techniques (doc2vec, BERT, etc.) on medical, pharmacy, and procedural codes. Daniel also collaborated with other data scientists to introduce working standards across the company and evaluate whether open source products could be used over premium AutoML services.

Daniel Nissani holds a Bachelors of Science in Education (BS.Ed.) from Northwestern University in the field of Secondary Education and Teaching in Mathematics. Daniel also holds a Master's degree from Cornell Tech in Information Science/Studies. Daniel is certified from the New York State Education Department in New York State Teaching in Mathematics Grades 7-12.

Daniel Nissani works with Rowan Cheung - Machine Learning Engineer, Max Cembalest - Machine Learning Engineer, and Valentine d'Hauteville - Machine Learning Engineer. Daniel Nissani reports to Keegan Hines, VP of Machine Learning.