Data Science Interview Assessments to Land Top Talent

June 1, 2022
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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. 

Data Science Assessments

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. Here are a few examples:

Propensity models 

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

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

  • Probability of website visitors to buy a product online

  • Likelihood of online shoppers to download a mobile app

  • Probability of loyal customers to refer 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 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. 

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

After the test, engineers can discuss candidates’ approaches and thought processes while solving the challenge.

Data Cleansing And Enrichment

Cleaning dirty data and enriching it with external sources are critical steps for ensuring 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

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 of the challenge. This option allows interviewers to observe candidates in action and examine their approaches in detail. 

Data Analysis

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, candidates can be tasked 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 take advantage of 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. Finally, tests should cover multiple skill levels according to the position candidates are applying for.

Ready-To-Go Data Science Assessments For Technical Hiring

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 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