Data scientist is an incredibly important role that will have a big impact on the success of a business. Modern businesses use data to make important decisions in lots of different areas, including finance, marketing, and expansion. Because of how important this position is, it‘s critical that your business hires the right people for the job.
Good data analysts need to be well educated, have a lot of experience, and understand the relationship between data and the business. Finding a candidate who’s fully qualified can be difficult, and there are often lots of applications to sort through before a hiring decision can be made. One of the best ways to find the perfect employee for your business is through proper testing.
A data science test can reveal the capabilities of each candidate, giving you far more information than an interview or CV ever could. These tests are designed to explore the skills of the candidate and show you how effective they’ll be in the role.
Because the skills that data scientists need differ by company and even by the team within a company, assessing applicants should be customized. In general, however, data science is a multi-step process including a variety of abilities that add up to something more than the sum of its parts. In this article, we’ll be looking at why you should consider a data science test and how you can use them.
Why Use a Data Science Test?
A data science test can be used to reveal more information about a candidate. Here are some of the reasons why this is important:
- Data scientists and analysts play an important role in business decisions, so their skills need to be tested before hiring.
- Not all hiring managers will have a good understanding of data science, so a test will help to show whether a candidate has the required knowledge even if the hiring manager doesn’t.
- Testing helps differentiate between candidates who might have very similar CVs or experience levels.
- Custom testing will reveal whether the candidate has the specific skills they need to succeed in your role, as some roles will be very different from others.
Tests can usually be provided during the interview process or after an initial screening interview. In most cases, testing a candidate before they are invited to an interview is considered inappropriate as it can waste the candidate’s time.
Using a Pre-Made Data Science Test
Whatever data science skillset you want to assess, there is almost certainly a test for it. If you work in an unrelated field, in HR, or you just don’t have the required time and skills to create your own test, you can find plenty of examples online.
There are lots of different organizations offering data science candidate tests, which you’ll be able to choose the best for the role you need. Although the actual administration of the test and analysis of the data can be outsourced, there are some things you should ask yourself before setting up the test. For example, which abilities do you want to be tested, and how much time do you want each candidate to spend on the test?
Obviously, the exam results should indicate who is qualified for the job you’re hiring for, not for another career, so you want to make sure the right skills are included in the test. Also, you should always be considerate of the candidates’ time by not asking them to devote an entire day to your test.
Creating Your Own Data Science Test
If you’re hiring for a highly specialized role and you have a good understanding of data science, you might consider creating your own data science test. You should know the skills that the candidate needs to have to succeed in the role you’re hiring, including basic skills, specialized skills, and nice-to-have skills.
Some examples of skills and how they can be broken down include:
Basic Skills
- Python: data frame manipulation
- Python: scikit-learn model fit & predict
- SQL: Joins
- Missing value imputation
Specialized Skills
- Python: Keras or Tensorflow
- Python: time series with statsmodels
- SQL: stored procedures
- LASSO fit explanation
Nice-To-Have Skills
- Natural Language Processing Expert (NLP)
- Docker expert
You should then create a test using data that’s relevant to your industry in order to test these or other skills that you think your ideal candidate should have. The test should require the candidate to handle, analyze and interpret the data using the required skills, giving them a chance to prove their capabilities.
In most cases, tests shouldn’t be very long, but it depends on what you’re testing for, the type of data you provide, and the overall standards you have. An open-ended analysis of the dataset with some questions might only take three hours, while a machine-learning problem involving data cleaning and transformation could take 48 hours.
Once the test is complete, you’ll want to analyze the results and compare them to other candidates. Are the results in line with what you’d expect from a member of your current team? Did they use the correct techniques to come to their conclusions about the data, and was there anything they could have done better? These questions will help you to determine if they’re the right fit for your team.
