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Data Science 👨‍💻

Woven is excited to announce the first ever technical assessment built exclusively for experienced Data Scientists.

Why is vetting Data Science so challenging?

Data Science is much more than programming. It’s a spicy mixture of programming, statistical reasoning, the ability to quickly learn a business domain, and good, old-fashioned problem solving. Existing automated code quizzes don’t cover the important parts of the job. As a result, many companies have been forced to build their own Data Science exercises and projects in-house. Until now!

Because Woven uses real engineers to evaluate results, our assessment can cover messy, end-to-end Data Science scenarios. Scroll below for two examples. 👇

🕙 Time Limit: 35 min

Data Wrangling: Product Usage Drop-Off

For this scenario, the candidate must use:

  • Python: The number 1 programming language for Data Science work
  • Pandas: A very common library used by Data Scientists to perform various Data Science tasks in Python

Scenario problem:

Usage is dropping in your project management product, Mello. A colleague has a hunch about user location. They need your help to create repeatable analysis. Given some CSV data, write a script that generates reports based on certain criteria.

What you’ll learn about the candidate 💡

  • Can they use Python to correctly answer business questions about a data set?
  • Do they know how to manipulate data sets and handle missing values?
  • Are they proficient with grouping/filtering/querying pandas dataframes?
  • Can they work with unclearly labeled data sets?
  • How do they structure their code: extracting helper functions or putting pre-processing in a separate step? Do they improve code quality by using descriptive variable names and what/why code comments?
🕙 Time Limit: 50 min

Data Analysis: Product Usage Drop-Off

For this scenario, the candidate can pick from a variety of tools to complete the analysis including:

  • Pandas, NumPy, and Matplotlib (included in Qualified) 
  • Microsoft Excel
  • PowerBI
  • Jupyter Notebook
  • R

Scenario problem:

Usage is dropping off in your project management product, Mello. A coworker wants to know why. Given a CSV data file with some product usage data and a request for data analysis, write an email to a coworker to describe your findings. 

What you’ll learn about the candidate 💡

  • Can they visualize data to generate insights?
  • Do they explore various explanations for what could be going wrong?
  • Do they make recommendations for how to detect problems going forward?
  • How do they communicate written business insights to a coworker?
  • Can they adapt their language to effectively communicate with less technical teammates about data?
"As a data scientist myself, I have gone through many assessments in the past and I found yours to be particularly innovative. The focus on evaluating a candidate's potential to solve real-world problems, rather than their ability to complete mundane tasks, sets it apart."
Woven Candidate
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