Subject archive for "managing-data-science," page 4

Nick Kolegraff is the founder of Whiteboard, a strategic innovation company focused on machine learning and AI. Previously, Kolegraff was the Chief Data Scientist at Rackspace and a Principal Data Scientist at Accenture. As a part of Domino’s “Data Science Leaders at Work” guest blogger series, Kolegraff provides advice for data scientists and data science managers to consider when, or if, they decide to take a “chief data scientist” role. This advice includes insights on the mindset you need to have, the types of problems you need to solve, and the people you need to hire. There are three posts total. This second post focuses potential problems you need to solve.

Nick Kolegraff is the founder of Whiteboard, a strategic innovation company focused on machine learning and AI. Previously, Kolegraff was the Chief Data Scientist at Rackspace and a Principal Data Scientist at Accenture. As a part of Domino’s “Data Science Leaders at Work” guest blogger series, Kolegraff provides advice for data scientists and data science managers to consider when, or if, they decide to take a “chief data scientist” role. This advice includes insights on the mindset you need to have, the types of problems you need to solve, and the people you need to hire. There are three posts total. This post focuses on the mindset you need to have to be a “Chief Data Scientist”.

This blog post covers metrics that help data science leaders ensure their team’s work is aligned to business value.

We surveyed and interviewed some of our most successful customers to learn how they align their data science team with their business. The challenges involved are not technical, which is why the solutions may not come easy to data science practitioners and managers.

In this Data Science Popup session, W. Whipple Neely, Director of Data Science at Electronic Arts, explains why data scientists have responsibilities beyond just data science.

At the heart of the data science process are the resource intensive tasks of modeling and validation. During these tasks, data scientists will try and discard thousands of temporary models to find the optimal configuration. Even for small data sets, this could take hours to process.