Digitally transformed companies are expected to dominate the global economy in 2023 with a 53.3% contribution to worldwide nominal GDP, according to Statista. For companies who don’t want to be left behind in the digital age, knowing how to evaluate a data scientist is crucial for their digital transformation strategy. Learn how to evaluate a data scientist with these best practices!
Traditional methods of assessing data science skills are increasingly inadequate in today’s fast-paced, digital hiring landscape. Open-ended questions are no longer sufficient for evaluating technical skills and cultural fit. Generalized tests given as take-home assignments fail to consider important factors such as skills relevancy, time to completion, and validity of results, among others. Despite this, take-home tests are still necessary as a baseline assessment tool when utilized properly. The question remains for recruiters: ask yourself how to evaluate a data scientist practically and efficiently.
A better way to evaluate a data scientist is to use a technical hiring platform that streamlines technical assessments and integrates them into your company’s hiring process. Technical hiring platforms provide a framework for objectively, efficiently, and repeatedly evaluating data science’s complex core skills. These platforms can help recruiters make faster hiring decisions without compromising the accuracy of the assessment results. While utilizing technical hiring platforms is a vital factor in hiring qualified data scientists, it’s also essential to train recruiters to utilize the tools properly. These best practices use technical hiring platform tools to increase the accuracy of the assessment and automate aspects of the hiring process, which are tedious and time-consuming, so then recruiters can spend more time on the tasks that truly matter.
Given the breadth of skills data scientists need for their field, using general testing methods can lead to misaligned criteria, inaccurate results and subjective scoring. One myth is that data scientists don’t need to code (spoiler: they do, especially when working with R, Python, etc.), which may have led many hiring managers to approve a wrong hire.
Diversity is also important in the workforce. Encouraging applicants to showcase skill sets that may not necessarily be tested can benefit recruiters. By assessing the unique talents of data scientists, recruiters can base hiring decisions on filling current skill gaps within the department.
Therefore, companies need to find a way to measure data scientists’ core skills objectively. Technical hiring platforms help standardize skill assessment using tests that mimic real tasks such as:
Best-in-class technical hiring platforms compare candidates’ output against test cases to give interviewers an impartial look at performance. Tools such as artificial intelligence resume screening can give recruiters a better look at the candidate's previous experience and assess both hard and soft skills objectively.
Advanced platforms also automatically grade tests and rank candidates based on scores while obscuring personal information. When core skills are objectively assessed, decision-makers can better identify the right hire to lead their companies’ digital transformation.
Unfortunately, recruiters do not have unlimited time to assess candidates. Qualified candidates can get frustrated with long processes but also get frustrated with inaccurate assessments. Your current employees will also become frustrated if the open spots aren’t filled quickly because they will have to pick up the slack. This can cause high turnover rates, putting more pressure on recruiters.
So, do you know how to evaluate a data scientist fast? Having a fast-paced hiring process that accurately assesses candidates is difficult but not impossible. This process can be done with technical hiring platforms and tools. A dedicated platform for technical recruiting speeds up the evaluation of an applicant pool full of underqualified or inexperienced candidates. The platform weeds out unfit applicants through objective testing and leaves a select few for further assessment. HR can then present a shortlist of the best candidates for engineers and senior data scientists to interview. HR and senior tech talent are freed from the time-consuming assessment of numerous applicants and can focus on their primary duties.
By automating specific tedious tasks such as those below can be automated using sophisticated technical hiring platforms.
With an efficient technical recruiting process in place, companies can establish a high-quality, well-vetted remote workforce as tech talent increasingly chooses flexible arrangements with employers.
A unique aspect of how to evaluate a data scientist is that assessment tests are usually heavy and require specialized tools for data modeling, analysis, and other related tasks. Dedicated platforms take care of these technical requirements for individual assessments at scale, allowing companies to accommodate many applicants simultaneously.
Another best practice is to use environments similar to those on the job. Best-in-class providers enable this by integrating data science software like Jupyter Notebook and having candidates solve the challenge on the platform. Challenges can be supervised or recorded for later review by senior team members.
Examples of real-world data science challenges for applicants include:
Some of the best practices for evaluating data science applicants include an objective assessment of core skills, efficient evaluation of applicants, and using a repeatable testing process. A technical hiring platform streamlines these aspects of the hiring process. It ensures a higher caliber of talent hires using real-world tests emphasizing skill and expertise. Also, senior data scientists and engineers can use a technical hiring platform to quickly sift through an applicant pool and spend time reviewing only qualified candidates.
Sophisticated technical hiring platforms with data-driven technology will provide a way to assess candidates virtually utilizing virtual conference rooms that support pair programming, diagram building on virtual whiteboards, and more. These features and integrations can help candidates showcase their thought processes and will repeatedly emphasize the differences in candidate qualifications for better hiring decisions. Overall, the hiring process needs to consistently provide accurate results with objective analysis and encourage candidates to showcase unique skill sets while still providing a structure that can be repeated across multiple cases.
Learning how to evaluate a data scientist from scratch can be difficult without the benefit of skill-based technical hiring platforms. Although, not every platform is equal. The most sophisticated hiring platforms will include all the features mentioned above and more that can be utilized for assessing multiple different positions. Filtered streamlines data science assessments on a dedicated testing platform using Jupyter, Python-3, R, SAS, Java-8, and more. We have developed a unique scoring rubric that counts error numbers and compares applicant outputs to benchmark test cases. With Filtered, you can save time and align technical interviews with the position. Companies enjoy these benefits using Filtered’s dedicated platform:
Filtered is a leader in skills-based hiring. 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 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.
