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Accurately Predict Your Next Data Science Candidate On DoSelect

Ever wondered at the ease with which Amazon pops out the next book off their virtual shelves and accurately suggests you might love it? Or the way Netflix almost knows what your television preferences are? What drives this oh-so-pleasant experience for a user when they land at your web or mobile application? The heart of this answer lies in a technology which will outpace all technological breakthroughs in this decade - Data Science.

Dive into some of the many use cases of Data Science -  face detection, fraud identification, search optimization for the most accurate results in your preferred e-commerce website, emergency supply optimization before a Hurricane (Sandy), Data Science is omnipresent. Which leads us to the most obvious question - where are the enablers for Data Science?

Through the recruiters’ looking glass

What is required of aspiring Machine Learning Engineers and Data Scientists as their core acumen? According to the established professionals, the most important requirements are:

  • Fundamentals of Machine Learning
  • A sharp aptitude in statistics 
  • Good programming skills
  • A never-ending curiosity to understand and answer questions using data
  • An Intellectual adaptability to constantly learn the many domains and skills required in Data Science
An extensive research within the industry tells us that the Machine Learning workforce requirement within Data Science is growing rapidly. However, the rise of opportunities and workforce demands in the market are unfortunately not being met.

Why is this happening?

To gauge the various metrics that are vital in prospective Data Scientists and Machine Learning engineers, the current methods of assessments being used across the sector are –

  • Puzzles to gauge approach towards problems
  • Outsourced aptitude MCQs to know a candidate’s knowledge on their basics.
  • Written tests to gauge their mathematic or statistical skills
  • Solutions submitted as files to glean a candidate’s way of thinking and coding.
While the methods sound workable on the outside, when we peer deep into the system, we observe some serious, unanticipated glitches in it.

Due to a huge disparity between the expectations of hiring managers and the current assessment practices, the recruitment sector within Data Science faces serious hurdles, such as -

  1. The usage of Standardized methods that don’t fulfill requirements - The current methods of assessments in use leave the most important areas of assessment uncovered.
    For e.g.  A great programmer with zero statistical and analytical skillset would probably bag the job instead of a candidate that showed a good flair in all the areas, albeit, with a relatively lower score.

  2. Investing great amounts of training time on fundamentals - Post-recruitment, trainings happen across organizations to condition the Data Science and Machine learning workforce. Our research indicates that these trainings take up to a year to cultivate the desired skills – majority of which were sought for in candidates during hiring.

  3. Lack of sustenance in fresher hiring – Because of the aforementioned disparity, recruitments do take place in the standard way – often with a substantial number of recruitments - but give rise to difficulties in the sustenance of hired candidates on the job. 

The right kind of Data Scientists and Machine Learning engineers bring invaluable business insights at a larger scale, and intelligently manipulate data on a daily basis. With the requirements for a Data Scientist being unique in themselves, it is natural to match the hiring methods in this stream to its unconventionality.

Acknowledging this fact, DoSelect has brought in a novel assessment tool to help you find and know candidates - before you interview them. Our platform now enables Data Science assessments that incorporate Machine Learning assessment in it.

Within a typical ML or DS problem on our platform, a candidate goes through a variety of analysis in the steps below: 

  1. EDA - The most critical step in a Data Science problem is Exploratory Data Analysis.It is an approach to analyze the given data and determine its important characteristics. A mandatory EDA before proceeding with the problem – 

    1. Gauges a candidate’s capability of understanding the data without making any assumptions.
    2. Indicates how well a candidate can apply statistics and visualizations to the data.
    3. It helps evaluate their capability to formulate a valid hypothesis around the data.

  2. Data Plotting - Data plotting involves the representation of data sets using a graphical technique. This step illustrates a candidate’s understanding of the relationship between variables of the given data. 

  3. Testing for Accuracy - After they make a predictive model with the training datasets, a candidate’s thoroughness is observed when they test for accuracy and validate their models using a cross-validation data set that’s provided on our platform in the coding environment. of analysis.
On a given problem, a candidate is expected to carry out data analysis by exploring the given data set and then graphically plotting the data using Python or R. Post plotting, candidates engage in predictive modeling by making a data model and observing its errors and accuracy thoroughly. 

As our problem sets are based on real-world data, the plotted graphs and the predictive models help understand a candidate’s analytical mindset.

After a solution is submitted,DoSelect’s assessment engine checks the candidate’s solution against our test datasets. Based on the accuracy of prediction, that solution is accepted or rejected.

If a recruiter wants to compare the error metrics of four submitted solutions that have been accepted, they can observe the accuracy of each solution to figure out the best performers.

Data Science had its niche carved at inception, always following an entirely different path in the industry. When it comes to the process of recruitment within this stream, the use of standard methodologies can be gravely misleading to the recruiters. Imagine the kind of transformation one could bring if they found industry-ready Data scientists to hire and set sail. That’s the vision we’re busy trying to realize.

DoSelect currently supports Python and R for Data Science assessments.

What are your current Data Science and Machine Learning assessment blues? Write to us at and we would help you eliminate them in a cost efficient and technically exhaustive manner. Curious to know what this assessment feature can bring to your team, write to us at and we would set up a conversation.

Till next time.

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