Data science requires a complex set of technical skills that can never be really captured in a generic interview or a simple coding test. That’s why using real-world data science tests is a surer way of measuring skill than the traditional interview process when vetting candidates. Let's look at how skill-based tests help HR and team leaders in vetting data science candidates. 

Vetting Candidates Using Real-Life Tests

Hiring a data scientist can be costly due to the high demand for skilled professionals and tight competition in the job market. But hiring the wrong data scientist who lacks on-the-job skills is even more costly in the long run. Despite this, many employers and interviewers still use general tests which inadequately measure candidates’ technical skills. 

Companies competing in the digital world can gain better security for their data and competitive advantage by vetting candidates using realistic challenges. This approach to technical hiring saves HR a lot of time and resources and provides team leaders with better insights into a candidate’s data science skills. 

Here are a few ideas for vetting data science candidates:

Data Cleaning

Applicants can be given a sample dataset for cleaning. This task is common in data science jobs and prepares information for processing and analysis. Without this step, an analysis will produce erroneous results that can do more harm than good to an organization's data strategy. 

While there is no prescribed process for data cleaning, engineers can check applicants’ output for the following best practices:

• Use of data quality standards. Check if the candidate has a plan for cleaning the data set and if they use metrics as quality standards.

• De-duplication and removal of irrelevant categories. Check if candidates understand the nature of the dataset and recognize what doesn't belong there. Removing irrelevant categories/observations is a sign of efficiency in data cleaning. 

• Approach to structural errors. Observe how candidates identify and fix structural errors such as wrong category labels and inconsistencies.

Data Visualization

Data scientists need to know how to communicate data insights to a lay audience, whether C-suite executives or a consumer-facing sales team. When assessing data scientists' visualization skills, keep an eye on the following points:

• Clarity. Data visualization should not only be pretty but communicate business objectives clearly. 

• Empathy. Data scientists who take pains to know their audience and help them grasp the value of presentations are likely to be empathetic towards non-technical colleagues and superiors.
  
  
• Creativity. Data visualizations should tell a story compellingly that engages the audience's interest.  

Model and Metric Selection

Skillful selection of data models (and metrics) demonstrate a data science candidate's foundational understanding of their subject. Experienced candidates will likely display more confidence in their analysis and selection of a model. Some best practices to look out for include:

• Cross-validation for selecting the best model
  
  
• Efficient use of variables for interpretability
  
  
• Use of models with high prediction accuracy
  
  
• Use of appropriate metrics depending on the goal of the data science challenge
  
  
• Suitability to the given use case (i.e., ad hoc or recurring)

Feature Engineering

Senior data science positions are likely to require feature engineering to enhance existing predictive models. When vetting candidates using a feature engineering test, observe their approach to the following:

Use of indicator variables. See if candidates use indicator variables from thresholds or groups of classes to identify key data. 

Use of interactive features. Check if candidates know how to manipulate multiple feature interactions without errors or feature explosion. 

Representation of existing features. See if candidates can represent data to achieve an elegant and simple format. 

Error analysis. Observe how candidates approach error analysis through segmentation, algorithms, and other techniques. 

In summary, challenges that mirror real-life tasks are better at showcasing candidates’ skills than general coding problems. Skilled candidates who are not outspoken or prefer solving challenges on their own also have a greater chance to shine in a hands-on test than in an impromptu presentation. HR and engineers can build their teams better and secure top talent by zeroing in on skills through real-life data science tests. 

Vet Data Science Candidates with Filtered.ai Tests

Filtered.ai is redefining vetting in technical hiring. Our dedicated tests help you evaluate data science talent in detail with an immersive interview experience. Assess the following data science skills using our platform:

Filtered's data science skill tests take place in a live, standardized Jupyter environment where applicants work on real-life data challenges. We take care of the setup and provide ready-to-go datasets for the challenge. Prefer using your own data science challenge? You can upload that too on Filtered.

Vetting candidates has never been easier with Filtered.ai’s skill-based assessments. Interviewers can observe candidates in action using Live Rooms with video and/or code pad. Candidates also record themselves on video for interviewers to review on demand. Check out our features:

• Unlimited interviews
• Data science, machine learning, and artificial intelligence (AI) challenges
• Custom code challenges
• Live interview room with video
• Custom video questions
• Candidate authentication
• Plagiarism detection
• …and more

Filtered is a leader in skills-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. 

‍