5 Tips to Crack an Analytics Interview
Applying for positions in analytics? Interviewers typically look for the following skills for entry – mid level positions in analytics (3-4 years of experience):
For non-fresher positions, recruiters look to see if you have an awareness of issues that you are likely to face when dealing with business data and business problems.
Here are 5 tips to help you crack an analytics interview, specifically for entry to mid-level open positions:
1. Allocate most time in your preparation process for reviewing your knowledge of the analytical tools specified :
Be very proficient with the analytics tool specified: Most often for junior level positions, the most important criteria in an interview tends to be expertise with an analytical tool (like SAS or R). The emphasis tends to be around data processing and preparation. Spend time reviewing concepts of data import and manipulation, especially how to read non-standard data (mixed data formats, multiple input file types etc), how to join multiple datasets efficiently, how to conditionally select columns, rows or observations in data, and finally, how to do heavy duty processing, typically macros or SQL
2. If you have prior experience with analytics or data related processing or analysis, review the business process end to end as part of your interview preparation:
If you have prior work experience related to analytics or data, interviewers will certainly spend time asking you to explain the business process and the responsibilities of your specific role. They are looking for you to have a broad understanding of the end to end business process, and where your particular role fits in. It is important for you to show that you understand the source of your data, how it is processed, and how it is ultimately used.
3. Be prepared with at least two business case studies:
Interviewers will want to assess your knowledge of business analytics, not just the tool proficiency. Spend time reviewing analytics projects you have worked on if you have prior analytics experience or training. Be prepared to tell them what the business problem was, what were the data processing steps, what was the algorithm used for creating the models and why, and how were the model results implemented? You may be asked about challenges you faced at any of these stages, so do review issues and challenges in you past projects and how they were resolved.
4. Review statistical concepts:
Since analytical algorithms are based on statistical concepts, you will need to be prepared to answer questions related to fundamental statistical concepts, like hypothesis testing outcomes and rejection criteria, model validation measures, and statistical assumptions that need to hold for implementing different types of algorithms. A quick review of statistical concepts is a must as part of the interview preparation process.
5. Communicate effectively:
All the preparation in the world is not enough if you do not communicate effectively. Mentally practice answering mock questions. Focus on questions related to past experience and business process, with full answers so that you are not thinking too much on the fly at the actual interview. Of course you cannot anticipate every question, but if you spend time articulating answers to some questions, you will be better prepared with coherent answers.
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