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Emilie Schario on entering the world of data science, looking for jobs, working with customers, managing teams, interviewing people, and building a career.
Emilie Schario is a Data Strategist-in-residence at Amplify Partners. Previously, she was the Director of Data at Netlify, where she led 8% of the company's headcount, and was the first data analyst at many companies, including GitLab, Doist, and Smile Direct Club.
In this episode, we cover a range of topics including:
Emilie's journey into the world of data science:
- How she entered the world of data science
- Her learnings along the way
- Why Locally Optimistic is her favorite data community
Careers, Jobs, and Interviews:
- How can new professionals evaluate what area they like within data science
- How should a data scientist look for jobs?
- How do you interview people?
- What are some of the red flags during hiring?
- What should a data scientist do during the first 30 days of the job?
Culture:
- How should data science professionals talk to customers?
- What does good data science culture look like?
- How should first time managers think about imparting culture?
Current and future trends:
- What's your favorite resource for data science? And why?
- What has been the biggest positive development in ML compared to 5 years ago?
- Looking forward, what aspect of ML excites you the most?
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By Prateek Joshi
4.9
88 ratings
Emilie Schario is a Data Strategist-in-residence at Amplify Partners. Previously, she was the Director of Data at Netlify, where she led 8% of the company's headcount, and was the first data analyst at many companies, including GitLab, Doist, and Smile Direct Club.
In this episode, we cover a range of topics including:
Emilie's journey into the world of data science:
- How she entered the world of data science
- Her learnings along the way
- Why Locally Optimistic is her favorite data community
Careers, Jobs, and Interviews:
- How can new professionals evaluate what area they like within data science
- How should a data scientist look for jobs?
- How do you interview people?
- What are some of the red flags during hiring?
- What should a data scientist do during the first 30 days of the job?
Culture:
- How should data science professionals talk to customers?
- What does good data science culture look like?
- How should first time managers think about imparting culture?
Current and future trends:
- What's your favorite resource for data science? And why?
- What has been the biggest positive development in ML compared to 5 years ago?
- Looking forward, what aspect of ML excites you the most?
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