Two Big Ways Automated Assessments Help Organizations Hire Best-Fit Data Scientists

Data alone is useless to an organization, unless it is structured, organized and processed to generate actionable insights for business users. As organizations look to create value out of data, the demand for data scientists with strong technical, mathematical and story-telling skills is skyrocketing. The only problem: data scientists are hard to find. Almost 76,000 data analytics job roles remained vacant in India during 2017-18, according to a recent UpGrad’s Analytics and Data Science Jobs Survey. This is alarming given the 400% rise in demand for data science professionals across various industry sectors in India. As organizations look to capitalize on the wealth of data available today in the form of customer, partner, and internal data, it is critical to adopt a data-driven approach to evaluate and identify the best talent.

One such data-driven approach to recruiting skilled data scientists is the use of automated assessments to accurately assess candidates across skills such as critical thinking, communication, and machine learning. The result: ability to hire skilled data scientists who can help solve long-standing business problems, identify inefficient processes, and develop new revenue streams.

How can organizations hire strong candidates despite the existing talent gap? Here are two important ways in which automated assessments can help:

1. Leverage domain specific tests: Interviews alone are not enough when hiring data scientists. The reason: different interviewers use different questions to assess candidates, resulting in incomparable evaluations. No wonder leading companies such as The New York Times and Spotify are using standardized tests to hire data scientists. 

Automated assessments such as standardized domain assessments help measure candidates across wide variety of skills such as their ability to create solutions, implement new functionality and analyze code. Such assessments make it easy for companies to measure data science candidates on the fundamentals of machine learning, their aptitude in statistics, as well as good programming skills. Take a leaf from Airbnb’s hiring practices. The company assesses how candidates use data to solve every day problems faced by their internal data scientists as well as their ability to build a model and present it to an audience using effective communication skills.

2. Use customizable coding scenarios: Another critical skill for data scientists is writing and executing complex queries in SQL. Good data scientists should also have in-depth knowledge of python and Hadoop platform.  Experienced assessment providers can help companies choose from multiple automated coding scenarios to gauge data scientists’ domain related skills. The outcome: ability to hire strong data science candidates with deep domain knowledge and the ability to stay up to date in a constantly evolving field.

KAR Auction Services has been able to build a team of exceptional data scientists by using automated assessments to test candidates on SQL query and complex data analysis skills (including the use of R programming and Python).

Organizations are hungry for data talent but data science is a relatively new field and not all data scientists are the right fit for an organization. Automated assessments that are designed specifically to assess new age skills, such as those required for AI and Data Science roles, can add significant value during hiring. They support organizations across the skilling journey by discovering right-fit candidates, identifying skill gaps, imparting relevant knowledge, and finally evaluating and certifying them. The result: companies are strongly positioned to quickly recruit or retrain top talent - before the competition scoops them up.  

Written By

Arijit Banerjee