How to Evaluate Technical Skills for Data Scientists

July 14, 2022
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Reviewing engineers can quickly evaluate technical skills of data scientist candidates using take-home technical interviews.

While data scientist candidates can be interviewed and assessed using traditional methods, it can take a long time before an accurate portrayal of the candidate’s caliber is formed. In-person interviews go through many steps such as scheduling, initial meeting, test setup, technical interview, and scoring to assess each applicant. A take-home test is a better way of evaluating the technical skills of data scientist candidates. Here’s why. 

Why Take-Home Technical Interviews Are Best

Time is essential for assessing and securing data science talent. A take-home technical interview efficiently narrows a candidate pool to qualified applicants compared to a manual hiring process. In addition:

Take-home interviews save time. Senior engineers and data scientists need to spend time only with the best candidates instead of wasting hours interviewing dozens of applicants.

Take-home interviews assess multiple candidates simultaneously. Companies can take in more applicants per batch instead of one at a time.

Take-home interviews are convenient. Based on candidates' availability, take-home tests can be completed anytime, anywhere. This quality encourages candidates with a tight schedule to complete the test without sacrificing their priorities. Allowing candidates to complete tests at home also indicates that the potential employer values the employee’s work-life balance. 

Moreover, best-in-class technical interviewing platforms incorporate candidate authentication and fraud detection features to ensure the validity of results. 

Here are a few guidelines on evaluating data scientists' technical skills using a take-home interview:

Test Core Skills

Data science sits at the intersection of different fields, including mathematics, machine learning, programming, data manipulation, and more. Make sure your take-home test covers the core skills listed below. 

  • Model selection: Candidates should be confident in their model selection and know-how to validate accuracy, suitability, etc.
  • Metric selection: Candidates should display efficiency in using variables and the right metrics to meet the goal of the task/challenge.
  • Parameter tuning: Candidates should know how to control the behavior of a machine learning model and reduce error.
  • Data cleaning and enrichment: Candidates should use best practices to ensure data quality and robustness ex., deduplication, removal of missing fields, trimming of white spaces, and use of secondary data sources.
  • Data visualization: Candidates should communicate business objectives to a lay audience and C-suite executives.

  • Feature engineering: Candidates should be able to manipulate variables to improve existing models while reducing error.

Testing core skills is critical because many are attracted by the glamor and promising salaries of a data scientist’s job. However, not everyone who has visualized data using charts, graphs, etc., has adequate training and background in data science. Testing candidates for core skills immediately reveals who is qualified for the job and is likely to succeed. 

Use Real-Life Scenarios

If there’s one thing you should take away from this guide, it is to align the technical interview experience with the job's actual duties. Don’t take the easy way out and use generic or multiple-choice tests to evaluate your candidate’s technical skills. 

A better strategy is to leverage technical hiring platforms using real-life data science projects such as: 

Data modeling: Have candidates build a model for hotel reservations or product order management.  

Recommendation algorithm: Ask candidates to write algorithms that recommend similar products/services based on user history or lookalike audiences.

Data cleaning: Give candidates a 'dirty' database for error removal, deletion of duplicate files, or proper data segmentation.

Data analysis: Assign a dataset and variables for testing a business hypothesis using time-series analysis, A/B testing, etc.

Propensity modeling: Task candidates to predict the likelihood that a customer will buy a product online or how many VIP clients are likely to download a mobile app.

The best scenario is to model a data science test based on the nature of the company’s business. For example, reviewing engineers can assign retail-related challenges such as inventory database management or logistics planning using predictive analytics if a retail company is looking for data scientists. 

Explore Candidates’ Thought Process

After the test, it’s best practice to give candidates a chance to explain their approach to solving the challenge. Take-home interviews are best paired with video-recorded explanations so reviewing engineers and senior data scientists can follow candidates’ thought processes throughout the evaluation. 

At this point, you can begin determining culture fit. Observe how candidates planned their solutions, any errors they made, and the steps they took to fix mistakes. 

For live post-test interviews, reviewing engineers can use this portion to explore further the candidates’ job knowledge and grasp of their subject. Some questions to guide the conversation include:

  • Explain this complex part of the solution to me.
  • I saw you were a little stumped at this error. How did you overcome this difficulty?
  • What would you have done differently if taking this challenge a second time?

In summary, a take-home interview is the best way to evaluate the technical skills of data scientists. Reviewing engineers should test core skills to filter out unqualified applicants early in the hiring process. Data science tests are best patterned onto real-world scenarios for accurate skill assessment. Lastly, reviewing engineers should explore candidates' thought processes through a post-test interview or a recorded explanation. 

Evaluate Technical Skills For Data Scientist Candidates

Filtered offers ready-to-go data science assessments using Jupyter, Python-3, R, SAS, Java-8, and more to help you align technical interviews with the position you’re hiring for. We have developed a unique scoring rubric that assesses error numbers and compares applicant outputs to benchmark test cases. Our tests have fraud detection values to ensure the integrity of results. Furthermore, Filtered’s technical hiring platform enables live video so you can evaluate the technical skills of data scientist candidates in detail. 

Filtered is a leader in skill-based, data-driven recruiting technology. Our end-to-end technical hiring platform enables you to spend time reviewing only the most qualified candidates, putting skills and aptitude at the forefront of your decisions. We’ll help you automate hiring while also applying objective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo.