Michael Joseph Fox Email & Phone Number | The Machine Learning process is a living cycle that ends only when a model is retired. As such I approach modeling with the whole life cycle in mind. With years of experience developing my passion for empirical analysis in both academic and professional settings I have a high degree of competence in delivering predictive and prescriptive modeling solutions that meet the end-to-end technical specifications of stakeholders. My approach to data science focuses heavily on development of a minimal viable product with a focused scope, followed by iterative development, expansion, and evaluation. Key to the successful delivery of data science products is the ML-Ops process which ensures repeatability, explainability, and maintainability through robust pipeline engineering.

Michael Joseph Fox

The Machine Learning process is a living cycle that ends only when a model is retired. As such I app...
Eau Claire, Wisconsin, United States

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Michael Joseph Fox’s Email mfox@lexile.com

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Michael Joseph Fox’s Location Eau Claire, Wisconsin, United States

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Michael Joseph Fox’s Prior Industry Metametrics | Credit Suisse | North Carolina State University

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Machine Learning Architect

2024-07-01 00:00:00 - Present

Machine Learning Architect

2023-02-01 00:00:00 - 2024-07-01 00:00:00

Computational Linguist

2017-09-01 00:00:00 - 2018-11-01 00:00:00

Sociolinguistics Lab Coordinator

2015-08-01 00:00:00 - 2017-08-01 00:00:00

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Michael Joseph Fox is a The Machine Learning process is a living cycle that ends only when a model is retired. As such I approach modeling with the whole life cycle in mind. With years of experience developing my passion for empirical analysis in both academic and professional settings I have a high degree of competence in delivering predictive and prescriptive modeling solutions that meet the end-to-end technical specifications of stakeholders. My approach to data science focuses heavily on development of a minimal viable product with a focused scope, followed by iterative development, expansion, and evaluation. Key to the successful delivery of data science products is the ML-Ops process which ensures repeatability, explainability, and maintainability through robust pipeline engineering.