Kidus Hiwot Email & Phone Number | I'm a data scientist at Microsoft with experience in building and maintaining products and features that use applied statistics and machine learning as core functionality. I have recently also been implementing product features that use the latest LLM/generative AI technologies to simplify user experiences. Product areas where I've shipped features include marketing, supply chain and customer data platforms. Prior to Microsoft, I received my PhD in statistics. During my graduate career, I investigated statistical inference methods for epidemiological models that are nonlinear and non-Gaussian with spatial coupling. In general, I am passionate about the end-to-end process of formulating questions that are answerable, to a degree, using data and tracking the relevance of the original questions and insights over time. I am partial to time series analysis, panel data methods and causal inference.

Kidus Hiwot

I'm a data scientist at Microsoft with experience in building and maintaining products and features...
Ann Arbor, Michigan, United States

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Kidus Hiwot’s Email kasfaw@umich.edu

Kidus Hiwot’s Phone Numbers

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Kidus Hiwot’s Location Ann Arbor, Michigan, United States

Kidus Hiwot’s Current Industry Microsoft

Kidus Hiwot’s Prior Industry Google | University Of Michigan

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Data Scientist

2021-10-01T00:00:00.000Z - Present

Data Science Intern

2019-05-01T00:00:00.000Z - 2019-08-01T00:00:00.000Z

Graduate Student Instructor

2016-09-01T00:00:00.000Z - 2019-08-01T00:00:00.000Z

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Kidus Hiwot is a I'm a data scientist at Microsoft with experience in building and maintaining products and features that use applied statistics and machine learning as core functionality. I have recently also been implementing product features that use the latest LLM/generative AI technologies to simplify user experiences. Product areas where I've shipped features include marketing, supply chain and customer data platforms. Prior to Microsoft, I received my PhD in statistics. During my graduate career, I investigated statistical inference methods for epidemiological models that are nonlinear and non-Gaussian with spatial coupling. In general, I am passionate about the end-to-end process of formulating questions that are answerable, to a degree, using data and tracking the relevance of the original questions and insights over time. I am partial to time series analysis, panel data methods and causal inference.