How to Evaluate a Data Scientist: Best Practices For Hiring Top Talent

June 21, 2022
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How to evaluate a data scientist? Start by focusing on an objective assessment of core skills, efficient evaluation of applicants, and crafting a repeatable testing process

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. This article outlines a few best practices to keep in mind. 

How To Evaluate A Data Scientist 

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 skill 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.

A better way to evaluate a data scientist is to use a technical hiring platform that streamlines technical assessments and integrates into your company’s hiring process. Technical hiring platforms provide a framework for evaluating data science’s complex core skills objectively, efficiently, and repeatedly. Let’s take a closer look.

Objective Assessment of Core Skills

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.

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:

  • Statistical analysis
  • Data cleansing, enrichment, and wrangling
  • Data modeling and visualization
  • Artificial Intelligence and machine learning 
  • Deep learning

Best-in-class technical hiring platforms compare candidates’ output against test cases to give interviewers an impartial look at performance. Advanced platforms also rank candidates based on test 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. 

Efficient Applicant Evaluation 

A dedicated platform for technical recruiting speeds up the evaluation of an applicant pool which can be 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.

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. 

Repeatable Testing Process

A unique aspect of data science assessment is that 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 at a time. 

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:

  • Using a propensity model to estimate the probability of mobile users buying a product in-app

  • Predicting the probability of customer churn during a given period

  • Running an algorithm to recommend products based on a customer's purchase history within the last 12 months

  • Cleaning a dataset by correcting errors and deleting duplicates

  • Running a sentiment analysis on one of the brand's social media accounts 

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. Finally, 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. 

Hire Top Data Science Talent With Filtered

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:

  • 3X Faster Time-to-Hire

  • 50% Less Time Interviewing

  • 2X Better Interview-to-Hire Ratio

Learn how to evaluate a data scientist with Filtered today. 

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