Both technical and soft skills are important for data science professionals. So it’s no surprise that an interview gauges both skills of a data analyst.
Demand for data science professionals is skyrocketing as organizations are looking for all levels of data science talent to put them to work. Data analytics agencies, which are cropping everywhere in the world, need brilliant data analysts to help them solve their client’s pressing challenges.
While the way to assess a data analyst’s competency may vary across organizations, the result expected from their endeavors remains the same, to analyze, predict, and convey results to the C-suite. Unfortunately, many data analysts falter in interviews, especially when it’s their first time.
Understand the role
Typically, lead data scientist or senior data scientist interviews a data analyst, who starts by understanding the experience of the analyst. Data analysts should go through the job description and highlight areas important to the organization. An analyst should acquaint himself with the finest nuances of the tasks at hand. For instance, if the job requirements include data collection and cleaning, the candidate should have stories of how he tackled various challenges that came while data collection and cleaning.
Data collection is a tedious part of the job and companies want to know how comfortable the candidate is doing that part. Analysts are required to communicate the result of the data collection and analysis to leaders and managers. So they also want to learn how the candidate interacts with stakeholders to convey the results of the project. Thus, communication skills are as important as technical skills for data analysts.
Data analysts interview questions
Most often interviewers start with a problem-solving question. For instance, they could ask a candidate, “how will you predict X, when Y is known”. Naturally, both business acumen and knowledge of statistics come to help here. This is the part where the interviewer tests the skill application of the analyst.
Sometimes organizations hire analysts for future projects. In those cases, a candidate can face “solve for X” type of questions, for interviewers to ascertain the candidate’s skill to work on problems that are ahead in the organization’s pipeline.
Apart from these questions, an interviewer can ask the following typical questions.
- Tell me how you work with others in the business to gather inputs for your analysis.
- How do you communicate the results of your analysis?
- What skills are you working on?
These questions gauge the analyst’s commitment to their job and passion for data science. Best to answer these questions sincerely.
Finally, the interviewer may ask you questions that are directly related to the job. For example, if the role requires using R to analyze retail data. The interviewer might ask, how have you used R in sales and marketing. Big Data analytics is spreading across industries, understanding one sector of the industry inside out is helpful, but having knowledge of all functions within the sector yields better outcomes for data analysts.
Questions to ask interviewers
Understanding your needs from the job is just as important as understanding the psychology of interviewers. Getting through an interview is one thing, being happy in the job is another. Most interviewers leave candidates to ask questions.
For instance, a candidate can ask, “whether he will be working on a new project or a tried-and-done project”. The answer to this question will help the candidate understand the risk/reward position of the project.
Working on tried-and-done projects are often part of the expansion and have lower or no risk of failure. New projects are often truly research and development in nature that have a high risk of failure. Those who have a low tolerance for failure should aim expansion projects, while those who are comfortable with trial and error make a good fit for risky projects.
