Data scientists remain one of the most in-demand jobs in technology. With plenty of competition for top tech talent, companies need to move faster than the market and streamline their hiring process. e’ve narrowed down the three best practices to keep in mind when hiring data scientists so you can secure top talent before your competition does.

Best Practices for Hiring Data Scientists

Data scientists have been in short supply for years due to the growing demand for analytics and advanced technical skills. But competition is not the only culprit-—slow hiring funnels, inadequate skill evaluation, and bias can discourage candidates from going through the recruitment process and/or cause them to drop-off. Avoid preventable hiring mistakes and ensure your company is not missing out on qualified applicants by following these best methods for success: 

1. Write the Right Job Description

Good communication is key in building good relationships. Aim for clarity right from the start—your job description should be clear, concise, and informative. Let candidates know your exact expectations from Day 1. Here are some things you can expect data scientists to do for your company.

Another good point is to let the job posting reflect your company culture and way of communication. Whether businesslike, fun, or a mixture of both, a genuine voice will come through the message and appeal to a like-minded audience. 

The following are other tips for writing the right job description when hiring data scientists:

• State your 'must-haves' as early as possible, including desired competencies
• Steer away from jargon unless necessary
• Mention benefits and perks as part of the package
• Make it reader-friendly 

2. Improve the Interview Process

Now that the job description has attracted qualified candidates; it’s time to conduct the interview. A good interview is structured and based on a clear benchmark of position requirements. Here are sample questions you can ask when hiring data scientists:

• Tell me about coding languages in which you are most proficient.
• How did you solve a challenging data project in your previous position?
• How would you organize a large data set? What approach and tools would you use?
• What are your advantages over other qualified candidates applying for this job?

Keep in mind that potential top performers may fail the initial interview. That’s why the next part—technical skills evaluation—is critical. 

3. Evaluate the Right Skills

Data science is a skill-based job, but how many candidates are really given a chance to prove what they can do? Interview bias, pre-screening, and inadequate evaluation, among other things, can turn away top talent from your company’s doorstep. Therefore, employers should seriously consider shifting from resume screening to skill-based assessment. 

Well-rounded skill evaluation for data scientists is characterized by the following:

• Skill-based: Candidates should be given challenges that spotlight their skill and expertise. Therefore, provide applicants with a similar environment and tools they typically use for coding. 

• Objective: Scoring should be based on definite rubrics for evaluation. This definition eliminates bias and speeds up the assessment of multiple candidates. 

• Comprehensive: Assessment should cover major interdisciplinary areas instead of a single aspect. In addition, candidates should demonstrate their thought process and approach to solving the challenge. 

• Valid: The skill test should be fraud-proof and only produce authenticated results. That step will protect employers and genuine candidates whose potential may be overlooked due to fraudulent applicants’ fake results. 

Without these attributes, an evaluation will be ineffective and ultimately slow down the hiring process. 

Some of the technical skills to assess when hiring data scientists include:

• Data mining and visualization
• Data wrangling
• Statistical analysis and computing
• Mathematics 
• Proficiency in programming languages
• Machine and deep learning
• Predictive modeling

Filtered: Skill-based Hiring for Data Scientists

Filtered offers comprehensive and ready-to-go data science challenges including:

• Jupyter
• R
• Java-8
• Python-3
• SAS

To see all categories in the library or to ask about a particular challenge, you can visit this page. 

Filtered has developed a unique data challenge scoring rubric and experience for data scientists. Our technical skills assessment platform runs test cases against applicants' output which are scored by MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) compared to the correct answer. Candidates are evaluated based on their score, error number, and solution explanation. Filtered also enables live video and technical interviewing so that you can use more insight than an in-person meeting.  

Our end-to-end hiring platform is built for confidence and equipped with candidate authentication and anti-fraud measures. We have also made it our job to eliminate bias to focus on skills during the interview. 

Filtered helps you level up your process with skill-based assessments for hiring data scientists. We have helped companies fill positions in an average of five days and increased interview-to-hire from 4% to 60%. 

‍Filtered is a leader in skill-based, data-driven recruiting technology. We help you focus on skills, spend time reviewing the most qualified candidates, and streamline workflows. We’ll help you humanize hiring while also applying data-driven techniques to select the right candidates. To get started, contact our team today or register for a FREE demo.