Better Technical Hiring Practices When Recruiting Data Scientists

April 19, 2022
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Companies can find top data science talent with better technical hiring practices.

We live in the data-driven economy era accelerated by the COVID-19 pandemic and the resulting digital migration worldwide. Companies and organizations with no data strategy are in danger of falling behind and out of touch. But data science today is not without its challenges, and the key to overcoming them is getting top talent on board. Let’s look at how organizations can acquire top data science talent through better technical hiring practices. 

Technical Hiring Challenges

Hiring the right person can help your company overcome the following technical hiring challenges for data science talent:

  • Data Integration: Statista reports that nearly 60% of the world is connected online as of 2021, amounting to an estimated 4.66 billion Internet users. All of those active users generate data every time they access websites, open mobile apps, engage in social media, and make purchases online. The deluge of data from the connected world is swamping companies with no adequate infrastructure or talent to handle massive amounts of data. Without an internal strategy for data integration, valuable information is simply missed or lost.
  • Business and Cultural Fit: Data scientists are people who must work well not only with data but with business and organization leaders. Without a proper understanding of broader business and organizational goals, data scientists will inevitably build solutions that  do not solve existing problems. This claim does not mean that good data scientists must have a head for business themselves. Instead, data scientists need to be a cultural fit with companies and organizations to foster communication and collaboration with decision-makers. 
  • Talent Gap: The demand for talented data scientists remains high, with an estimated 31.4% employment growth from 2020 to 2030. Almost 20,000 jobs are expected to open up this decade but not nearly enough data scientists to fill them. Companies need to develop their technical hiring practices to find and acquire talented data scientists, given big data’s increasingly important role in the global economy.

Acquiring Talent Through Better Hiring Practices

In a world where technical talent is scarce and in high demand, actively looking for data scientists is not enough. Companies must be prepared to engage them and win them over with better hiring practices. Here are a few tips to ensure qualified candidates don’t slip through your fingers due to an inefficient technical hiring process.

Improve The Technical Interview Process

The best technical interview process isn’t a process anymore — it’s one fluid assessment flow that condenses multiple steps such as phone screens, scheduling, in-person interviews, and technical whiteboarding. By removing unnecessary steps and starting with a skills-based first approach, the interview experience is instantly improved for all parties involved. It also ensures that HR and recruiters are identifying only the most qualified candidate and allows hiring managers to devote maximum time to skill assessments where data scientists and technical hires shine best. 

Companies can also improve the technical hiring process by structuring interviews with predetermined questions. By asking the same questions, interviewers can better compare candidates’ responses without letting personality or other characteristics influence their judgment. Below are sample questions for data science candidates:

  • What coding and programming languages are you proficient in?
  • Share about a time that you tackled a difficult data project. How did you solve the problem?
  • What is your approach to cleansing or organizing a massive data set?

Evaluate The Right Skills 

Technical interviews are primarily performance-based and use a skill as the main criteria for assessment. Therefore, companies need to provide candidates with appropriate coding tools and environments to evaluate the right skills for the job. 

Below are some technical skills data scientists must be able to demonstrate in a data science challenge:

  • Data mining
  • Data visualization
  • Data wrangling
  • Mathematics
  • Statistical analysis
  • Predictive modeling
  • Machine learning

When evaluating data science challenges, hiring managers should create and use definite rubrics for scoring to avoid bias and speed up the assessment of numerous candidates. 

Expand Candidate Pools

For a technical interview to happen, there must be a candidate on the other end. Qualified applicants can quickly become scarce on traditional recruitment networks as employers vie for the same people. Expanding candidate pools gives companies a higher chance of securing qualified data scientists. Here are a few tips to get started:

Write skill-oriented job posts. The first thing that candidates see is the written job post. If they don’t feel connected to your company’s message, they will move on. Making job descriptions skill-oriented, such as, “Looking for a data scientist with proven experience of application frameworks such as Angular and Django,” will immediately appeal to a candidate’s skills. Generic job posts that emphasize non-negotiables like “Looking for a data scientist located in Brooklyn, Master’s Degree in computer science a must” will turn away equally qualified data scientists who feel they do not meet a very narrow set of criteria.

Welcome flexibility. Unless necessary, do not limit applications to candidates who can commit to a full-time job in the office. COVID-19 has opened the world’s eyes to the benefits of remote work, and many people are not returning to a 9-to-5 arrangement anytime soon. Consider offering flexible working arrangements to applicants, especially those who have dependents or are physically disabled. Flexibility should also be offered for technical interviews and assessments to encourage applicants. 

Tap into networks representing diverse groups. Consider partnering with non-mainstream recruitment networks like government programs, colleges and universities with diverse student bodies, and organizations dedicated to promoting underrepresented workers. 

With these technical hiring practices, employers and recruiters can take the first step to beating the data science challenges that face companies today in the data-driven economy. Hiring better data scientists begins with refocusing the technical interview process on skill-based assessment. Comprehensive and fair evaluation ensures candidates are hired for their skills and not non-negotiable factors such as education, age, location, etc. Employers also increase their chances of securing data science talent by expanding their candidate pools and considering diverse talent. 

Hire Data Scientists With Skill-Based Technical Assessment

Filtered offers ready-to-go data science challenges such as Jupyter, Python-3, R, SAS, Java-8, etc. We have also developed a unique scoring rubric for data challenges that assesses error numbers and compares applicant outputs to benchmark test cases. Furthermore, Filtered’s technical hiring platform enables live video and technical interviews to help determine whether candidates are a good culture fit. 

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 subjective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo.