A Guide To Data Science Assessments: Land Expert Talent

The best data science assessments reflect actual tasks and challenges on the job

Data science remains among the top sought-after jobs in the United States (currently #3 in Best Technology Jobs) with some of the most complex skill sets. Through skill-based interviews, data science assessments help engineers and team leaders land high-quality talent. Below are several examples of data science tests you can use in your next technical interview. 

Looking for a specific skill to test? Skip around!

  1. Propensity Models
  2. Recommendation Algorithms
  3. Data Cleansing and Enrichment
  4. Data Analysis

Useful Data Science Assessments: Testing Skills and Techniques

Data flow illustration to symbolize models for assessing data scientists

Data science requires a combination of statistical skills, quantitative analysis, programming chops, and the ability to manipulate data. Engineers looking to build their data science teams need to focus on tests that probe the depth of a candidate's expertise and creativity. It's best to skip generic problems and instead select tests relevant to the nature of the company’s business. 

Real-world problems can be assessed utilizing technical environments that mimic virtual work environments. These tools create a simulated environment for candidates to test on-the-job adaptability, scenarios, and expertise, providing more reliable insight for recruiters to utilize when making those tough decisions.

The skills that recruiters should test in data science assessments are propensity models, recommendation algorithms, data cleansing and enrichment, and data analysis skills. 

  1. Testing Candidates: Propensity Models Skills

Propensity models applied to customer trends are useful for retail enterprises, CPG companies, and other consumer-facing industries. These models also inform personalization and marketing strategies increasingly being adopted worldwide. 

Some examples of propensity modeling that can be used to test candidates include:

  • Probability of website visitors buying a product online

  • Likelihood of online shoppers downloading a mobile app

  • Probability of loyal customers referring another customer

  • Likelihood of a cold/warm lead to respond to an offer and convert

  • Probability of churn

Again, it's best to pattern data science assessments to the nature of the company so team leaders can better assess what a candidate brings to the table. 

Interviewers should select the real-world scenario and provide the requisite variables for modeling. Engineers can further use test cases to evaluate a candidate's output. Alternatively, technical hiring platforms give ready tests, tools, and scoring rubrics for faster evaluation. This includes the use of virtual whiteboards for diagram building and planning. 

  1. Testing Candidates: Skills in Recommendation Algorithms

Recommendation algorithms are widely used on eCommerce sites, service-based offerings, financial platforms, and even social media. Data science assessments can focus on the following methods and scenarios:

  • Content-based filtering. Recommend items based on purchase or content consumption history within a definite period. 
  • Collaborative filtering. Recommend products or services based on a similar audience segment's purchase or consumption history.
  • Natural language processing. Recommend products or content which have similar features.

These data science assessments typically require a sizable amount of user information and behavior history to generate relevant recommendations. 

Testing this skill can be done utilizing video answers on take-home tests so that candidates can talk through their answers. This also encourages candidates to showcase other skills, such as communication, comprehension, confidence, and solution-oriented thinking. While candidates work through problems during the screening process, candidate profiles can save their answers for recruiters to assess later when making difficult decisions. After the test, engineers can discuss candidates’ approaches and thought processes while solving the challenge with virtual conference rooms built for pair programming and engineers. 

  1. Testing Candidates: Data Cleansing And Enrichment Skills

Cleaning dirty data and enriching it with external sources are critical to ensure accurate data analysis and results. Candidates applying for a junior role in the team can be given datasets to accomplish the following:

  • Correct/remove errors

  • Delete duplicate records

  • Remove forms with missing fields

  • Filter outliers

  • Trim white spaces

  • Fix conversion errors

  • Merge several data sources
  • Segment data by demographic/psychographic/behavioral information

  • Derive attributes such as date/time conversions, dimensional count, etc.

  • Extract entity from unstructured data

Integrated development environments (IDEs) from data-driven recruiting technology can mimic on-the-job environments to test candidates in realistic scenarios for the best results. This also helps keep fair evaluations across candidates. Since every IDE provided different features, if each candidate used a different IDE for their test, some may seem more qualified than others due to the extra features. This can skew the results and disrupt hiring decisions.

During the test, engineers can check for best practices like sorting by attribute or breaking the dataset into smaller chunks to increase speed. 

If the test is not live, i.e., take-home challenges, engineers may request a video recording. This option allows interviewers to observe candidates in action and examine their approaches in detail. 

  1. Testing Candidates: Data Analysis Skills

Ad hoc analysis helps organizations and enterprises turn data into actionable insights. A data scientist analyzes data to determine trends and patterns that can guide marketing initiatives, improve sales performance, and enhance customer experience. 

Using a dataset and given variables, you can ask candidates to test business or market hypotheses using the following methods:

  • Time series analysis

  • Regression analysis

  • Predictive analysis and modeling

  • A/B testing
  • Sentiment analysis

  • Machine learning techniques 

To complete the selected data science assessments, engineers need to provide candidates with tools such as Jupyter, Apache Spark, Tableau, and others. Team leaders can also utilize dedicated hiring platforms that set up data science assessments with the right tools and environment. 

In summary, engineers and team leaders can better land first-rate engineering talent using skill-based tests in technical interviews. Data science assessments are best patterned on actual roles and duties on the job instead of generic problems. By simulating real-world scenarios in virtual environments, recruiters can make better hiring decisions that impact future turnover rates and fill skill gaps across departments. Finally, tests should cover multiple skill levels according to the position candidates are applying for. With data-driven recruiting technology and tools, recruiters can easily test candidates on hard skills and soft skills at the same time.

Filtered: Ready-To-Go Data Science Assessments For Technical Hiring

Virtual interview tools with skills-based hiring can help fasten the pace of the evaluation process while increasing the amount of data and analytics collected from each candidate. 

  • Artificial intelligence resume screening can eliminate bias at the start of the hiring process and assess candidates' skills before the interviews in a fraction of the time it would take a team of recruiters. 
  • Candidate profiles save the candidates' answers so recruiters can view information about potential employees when making the final decision. 
  • Identification tools eliminate fraud and cheating in the hiring process.
  • And more…

Recruiters don’t want to switch between numerous tools during the interview process. These tools are meant to automate tedious tasks so recruiters can focus on the candidates. Having multiple tools to switch between defeats this purpose. Finding a skill-based recruiting platform with all the features and tools mentioned above seems difficult, but luckily Filtered has the technology you need. 

Filtered offers ready-to-go data science assessments using Jupyter, Python-3, R, SAS, Java-8, and more to help you align technical interviews with the position you’re hiring for. We have developed a unique scoring rubric that assesses error numbers and compares applicant outputs to benchmark test cases. Our tests have fraud detection values to ensure the integrity of results. 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 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.