Automatically rank data science candidates with easy-to-set-up assessments
- Test for proficiency in data science principles, data analysis, SQL, Python, and R.
- Use multiple-choice and free-form questions, programming exercises and gamified challenges.
- Pick the best candidates to go forward with, based on their skills.
Design and customize the interview questions so they’re relevant to the role
- Establish a PostgreSQL or MySQL database with relevant data for the candidate to query.
- Generate a dataset in any format, such as JSON, CSV or Parquet or any other preferred data type.
- Configure a specific environment for your interview ranging from Jupyter Notebook (SciPy, Minimal, R, TensorFlow) to specific language environments like Python, R, or SQL.
- Conduct your live collaborative interview, or deploy your project as a take-home (asynchronous) project.
Give candidates a fully functional, realistic interview environment including:
- Ample processing power to handle super large data sets.
- A fully-functional Jupyter Notebook environment.
- Ability to import and utilize any package or library.
- Plot data and run individual cells in the notebook.
- Access to the server, shell and console.
Collaboratively review candidates as a team and build a world-class organization
- Private interview notes capture the hiring team’s feedback.
- Save the candidate’s final output for objective review.
- Confidently make your next data science hire(s).
Jupyter Notebook Online Sandbox
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2 Interview Questions for Vetting Data Science Candidates
Read on to find two curated technical interview questions designed to help you assess the expertise and problem-solving abilities of your data science candidates.
Mastering Jupyter Notebooks: Best Practices for Data Science
Whether you’re new to Jupyter Notebooks, or an expert, you’ll find some useful tips to use in your next data science interview.