Technical Assessment

During the recruitment process, the purpose of the technical assessment for a data scientist role is to measure the candidate's analytical skills, problem-solving capabilities, and comprehension of data science principles. It enables the employer to assess the candidate's expertise in areas pertinent to the role, such as statistical analysis, machine learning algorithms, data visualization, and programming languages like Python or R. The primary objective is to confirm that the candidate possesses the required skills to execute the data scientist role effectively and can add value to the team. This assessment may also evaluate the candidate's understanding of data cleaning, data manipulation, predictive modeling, and their ability to interpret and communicate complex data findings in a clear and understandable manner.

Screening

Screening happens usually early in the process and is all about filtering with high precision. Therefore, the process tends to be more oriented to not miss any potentially good candidates.

Questions

Example simple questions given to test standard conceptual knowledge are provided. These provide an example of the range of basic knowledge required at this stage.

Implementation

Working with data, gathering, cleaning, manipulating, and extracting relevant insights that can be well represented graphically or verbally is at the heart of a successful data scientist. Representative implementation examples.

Problems

During various stages of the process, you might be presented with a problem that mirrors a simplified real-world situation. If there's an onsite interview, anticipate encountering several such problems. Your solutions will be assessed for their effectiveness. To gauge the level of difficulty and prepare accordingly, examples of real-world problems are given.