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The industry leading knowledge and insights from this group have folded into Codesmith's programs.
Learn More Here
If you are driven, curious and want to make an impact, there is no better space to be involved in than Data Science and Machine Learning. Our organization is designed to help talented practitioners and students do impactful work by building meaningful connections that allow you to connect with companies and projects that change the world.
If you want to build with other people who share the same goals, you've found your home.
Our 9 week fellowship enables people with quantitative backgrounds to transition into commercial data science.
Build data products from end-to-end. Master the entire stack.
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Architect projects that have real stakes and require cross disciplinary collaboration.
White glove service that caters to your academic background.
The Codesmith group of companies has an established track record of partnering with some of the world’s top companies to help solve their tech problems.
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"The DSML team was incredibly talented and responsive. They helped us build an innovative suite of tools to assist decision makers with analyzing and negotiating complex challenges. The team both met our needs and delivered at the cutting edge, creating new AI capabilities that broke new ground."
Tim McDonald, Earn Edge
We're a team of practitioners, thought leaders, builders and industry pros aiming
to collaborate and expand the frontier of possibilities in their domain.
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Professor, Columbia University
Gerard is an assistant professor of Sociology at Columbia University and a member of their Institute for Data Science. His expertise is in the area of causal inference and experimental design.
His work has been published or is forthcoming in the American Sociological Review, Child Development, Demography, Housing Policy Debate, the Journal of Housing Economics, the Journal of Urban Economics, and the Proceedings of the National Academy of Sciences.
He helps to contribute to DSML's research endeavors.
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Principal Engineer
Alex worked as a deep learning engineer for Amazon and Uber, where he contributed to their internal ML libraries such as MXNet, which were used to power their AI products, with a specialty in Computer Vision.
He's also the author of the book "Deep Reinforcement Learning in Action" by Manning Publications, which was the first book to emphasize the use of RL for common engineering problems.
He specializes in developing the program and assisting with DSML engineering endeavors.
Principal Data Scientist
Jonathan is a data scientist with an expertise in time series modeling and open source development. He's helped organize contributions to TensorflowJS and sktime, and enjoys evangelizing the spread of grassroots ML knowledge.
He's worked with organizations such as General Assembly, NYPD, Amber Capital and Advent International to assist them with their data science needs.
He has a particular passion for time series problems, since he believes
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