A data analyst usually works as a part of an analytics team or business unit. While working with the analytics team, the analyst often ends up in performing tasks like dashboarding, extracting data, building recurring reports, performing exploratory analyses and more. The most critical skills for a data analyst include the knowledge of databases, languages like SQL, Python, etc., a good understanding of business and data.
Almost 75% job roles in an analytics function comprises business intelligence, data mining and extraction, visualization etc. Someone who is starting a job with an analytics function, especially with large firms, there’s almost 90% chances that they would be doing pure data extraction and presentation related work, atleast for the first 1-2 years. High stakes roles like creating models, algorithms, presenting to the clients and business units is often left for more senior professionals in the team.
There’s also a 90% chance that while interviewing for the same roles, these early starters would rather be asked more complex questions, grilled especially on model building etc. We have seen recruiters not grilling early candidates on data preparation, visualization skills as much as on algorithms.
This leaves a void, almost bordering to confusion, on what the candidate should get themselves prepared for. One would argue that over time, these candidates would have to move to more complex roles and it’s good to build those competencies early on.
To get an industry perspective on this, Analytics India Magazine caught up with a few experts in this field who explained where the interview gets flawed and helped in understanding how these issues can be addressed.
Recurring Bottlenecks And Pain Points For A Candidate Appearing For A Data Analyst Interview
There are many challenges that data analysts face, which may make them uncomfortable or not entirely prepared for these interviews. We took inputs from the industry on some of the bottleneck and pain points that candidates in analytics interviews mostly face.
Varun Hasija, Director of Product Development at Doceree shares that there are mainly three bottlenecks or pain points that a candidate has to face:
- They have to prepare for multiple different companies with different resumes highlighting their capabilities.
- The high-pressure anxiety-inducing whiteboard exercises that they have to go through.
- Even after appearing for jobs and interviews, they have to maintain a positive outlook even when an interview does not translate well. This becomes difficult at times when a candidate has to appear for multiple interviews within a day or week.
Not getting enough opportunity to develop soft skills is another pain point. “The recurring bottlenecks and pain points for a candidate appearing for interviews are still related to soft skills, particularly communication, creativity, teamwork, etc. This perceptual divergence of what companies want and what candidates perceive they must have as skills tell upon the interview performance of the latter,” shares Naveen Das, Dean Academics and Dean School of Business and Economics, Adamas University, Kolkata.
RB Jadeja, Dean of Engineering, Marwadi University, believes that insufficient experience in dealing with real-life data analytics problems/projects is a significant challenge. “Many candidates face challenges such as inability or less efficient ways to create interesting storytelling with data and not considering themselves skilled in the application of all tools and concepts required for a data analyst,” he said.
“The interview questions for Data analyst focus not only on the analytical skills but also on the “soft skills” such as communication and compassion. Over the past years, data analysts have become crucial to companies’ long-term policies that involve a range of tasks, such as writing algorithms and connecting with the C-suite in the afternoon,” said Abhishek Latthe, Founder and CEO of SenseGiz Technologies.
Private Interview Setting v/s Public Interview Setting
Like in other domains, data analytics interviews are conducted in public and private interview settings based on different companies. While public interview setting involves several candidates in the interview process, private settings are about a single candidate at a time approach during the hiring process. We tried to understand which process yields better hiring.
“Hiring data analysts are critical as they form the foundation for the Data Science function of the organisation. We usually follow a two-step process — pre-discussion assessment based on the alignment of the candidate’s profile and work experience and personal discussion in a private setting, to understand the strengths of the candidate better,” said Abhishek Singh, Chief Analytics Officer at Lendingkart. He said that they try to look for the right mix of attitude, structured problem-solving approach and quantitative or data management skills.
“Given the importance of interviews, conducting the initial interview in-person allows a more natural, spontaneous conversation than what may be possible through the telephone or by email. On the other hand, holding interviews at the workplace building may invite accidental revelation or reflections on why continuous people are leaving and returning to their work areas at regular intervals. I believe both the forms of interviews are important as per their context,” said Latthe.
Das explained that while public interview setting has become very popular lately due to the comfort it provides to both the interviewer and the interviewee, one must be careful that the environment should not impinge upon the need for privacy or anonymity of the interviewee. Nowadays, with issues like maintaining the confidentiality of the candidates appearing for interviews, etc. becoming important, companies are preferring to hold interviews in public settings or off-site locations. In his opinion, the basic norms of providing comfort, privacy, confidentiality and conducive environment for candidness must be adhered to in an interview.
“I believe that a private interview setting may yield better results because of its very nature of giving comfort to the candidates and letting their thoughts come out without fear of public scrutiny and ridicules. Also, sometimes an informal conversation in interviews helps candidates to melt down their stress and nervousness, which in a way helps the interviewer to extract relevant information related to the position’s profile,” shares Jadeja.
How To Overcome The Flaws
Many analytics professionals believe that early-stage data analysts often face complicated & core ML questions, irrelevant to the daily job practice as a data analyst. To avoid this, Latte advised that candidates must read the job description thoroughly.
“This seems like rudimentary advice, but you’d be amazed at the number of people who haven’t taken time to do this. First and foremost, a data analyst must comprehend the finest shades of the task at hand, for instance, come ready with stories of how you did in your earlier roles. Make sure also to come prepared with an enhanced data analyst resume that emphasises what abilities or practices that would make your ideal for this specific role,” he said.
Hasija shared that most companies follow a” one size fits all” approach, and it is necessary to consider the daily problems that they have to solve, such as data cleaning practices. “Instead of assigning high-pressure white board-based exercises without any external help, one should test the candidates for being methodical and having high-tolerance for repetitive tasks in their work,” he added.
Das echoes that this is a flaw prevalent in almost all professional interviews and not just in case of data analysts. The root cause is the rigour-relevance conundrum in the profession. Since interviewers are alert to the lack of practical experience of early-stage candidates, barring few rudimentary internships, it is but natural that core algorithmic questions are asked in interviews.
He further added that the understanding of the customer’s context, data generation and flow, the veracity of data, validity and reliability of data with practical insights, etc. must be tested for greater fit and relevance of the data analyst’s job.
On the other hand, Jadeja has a different take. “I think, the right attitude, programming skill, reasoning and logical thinking, should be given preference for a job of a data analyst; nevertheless, I would like to ask candidates some basics or general algorithmic questions at the time of interview to know how familiar and aware the candidate is about the terms and usability of data analytics,” he said on a concluding note.
