The purpose of the Data Science Interview Series is to survey successful data scientists and machine learning experts to collect and share insights, resources, and best practices, and to help aspiring and professional data scientists succeed in their journey.
The knowledge and information shared are intended to be used as a reference for:
- Beginners and aspiring data scientists who want to move their first steps into data science and machine learning.
- Data science practitioners and professionals who want to keep themselves up-to-date with the best practices and gain perspectives on other professionals’ workflows.
Are you interested in sharing information and knowledge about AI and connecting with some of the brightest minds in the field?
Learn about joining as an Associate Editor.
Share your story
After the sign-up through the form below, we will get in touch via email with selected candidates to proceed with the interview.
Do you have a story to share?
Who will be interviewed?
Experienced data scientists, machine learning, and deep learning engineers who are willing to contribute to the community by sharing their knowledge and expertise.
Why should I participate?
Participating by being interviewed represents an opportunity to contribute to the community and help others by sharing your insights, best practices, and resources that helped you along your journey while showcasing your profile as an expert in the field.
Is it free for interview guests to participate?
Yes, this initiative is supported with advertising (see disclosure here) and it is free for the interview guests to participate.
What topics will the interviews cover?
Each interview article will typically cover some (or all) of these topics:
- Interviewee introduction: where they work, projects they are involved in, how they got into data science / machine learning.
- Challenges faced by professional data scientists, how they overcome them, and how they constantly improve their workflow.
- Skills that data scientists use on a daily basis (e.g. math, coding, domain knowledge), how they acquired them, and how they develop them.
- Data science resources, courses, books, and inspiring people to follow that help you along the way, favorite machine learning events and conferences, etc.
- Work-life balance as a data scientist.