I’m assuming you’re already comfortable with and love working with data. Below I have highlighted some strategies / tips which can help you with preparing for any analytics job interview based on my interview experiences with a lot of firms including CVS Health, Facebook, Goldman Sachs, AbbVie etc. For the purposes of this article I will not focus on behavioral / fit interview prep (as that is pretty easy to do with plethora of resources on the web) but more towards the technical and business sense portion of the interview.
1. First and foremost, consider what problems your target company may have that you can solve by taking on this role.
Similarly to how not every CEO’s function is the same, not every data analyst’s role is the same. Different companies at various stages will have a different set of data challenges. There is no such thing as a one-size-fits-all approach to interview preparation.
Remember, they’re hiring you to solve their problems. So if you can solve them, you’re hired. But that begs the question: what problems?
- When you have a specific company in mind for which you are applying, please conduct thorough research on the organization first:
- What is their business model, and how do they generate revenue?
- What are the most critical KPIs to track in their industry?
- What are their potential data issues at this stage?
- What are their customers like?
Some of the above questions can also be asked to the company when interviewing with them.
Once you know about the problems that your target company is having, you can simply prepare in a directed manner while keeping the problems in mind. Remember this preliminary research will tremendously help you with business cases that you might get in your interviews. Also be prepared to answer to the cliched “Why this Company?” question.
2. Spend the majority of your preparation time reviewing your knowledge.
Review your skills across these three aspects: Business sense, Numbers sense, and technical skills.
a) Business sense:
So an analytics consultant is not just a technical role, to become one you should have a good business sense. If you have prior work experience in analytics or data, interviewers will probably spend time asking you to clarify the business process and your individual role’s duties. They are looking for you to have a broad understanding of the end-to-end business process, and where your particular role fits in.
A good business sense is not something that comes naturally. It demands experience, thought, and a plethora of trials and errors.
1. In the data report, what numbers should I show that make the most sense?
2. What types of KPIs should be used to assess how well the business is doing?
b) Good data/numbers sense:
A good analytics professional will be required to make sense of the numbers. Knowing how to interpret data in a report allows you to explain and paint a picture of the present situation.
A person with good numbers sense will be able to ask the following questions (for e.x):
- Is the conversion rate too low?
- This number looks off, is this calculated wrongly?
- Why are the booking numbers decreasing despite the recent advertisement spending?
c) Good technical skills (Database, SQL, Python, R)
Analytics folks must have strong technical knowledge because numerous tools use different programming languages. It will be advantageous to be familiar with as many languages as possible, with Python and R being the most popular.
Additionally, SQL is the database management language you must concentrate on. SQL is the most essential data language in the field of analytics, and it will only become more popular in the future. Spend time reviewing data import and manipulation concepts, particularly how to read non-standard data (mixed data formats, multiple input file types, and so on), how to efficiently join multiple datasets, how to conditionally select columns, rows, or observations in data, and finally, how to do heavy duty processing, typically using macros or SQL.
At any point of time, if you forget the syntax, be honest with the interviewers. Write down whatever you know and move on. Your interviewers will most certainly be aware that, despite your thorough understanding of SQL joins, you could always look up the syntax. And If you make mistakes, don’t let them hold you back. Let it go, smile, and go on.
It is also necessary to have some theoretical technical expertise. Analytical algorithms are based on statistical concepts, you must also be prepared to answer questions about fundamental statistical concepts such as hypothesis testing outcomes and rejection criteria, model validation measures, and statistical assumptions that must be met in order to implement various types of algorithms. As part of the interview preparation process, a quick review of statistical concepts is required. And interviewers might be asking whiteboarding solutions, so make sure you practice a few problems on the whiteboard before your interview.
3. Have at least two business case studies ready:
Interviewers will want to assess your knowledge of business analytics, not just the tool proficiency. If you have past analytics expertise or training, spend some time examining analytics projects you have worked on. Prepare to explain the business challenge, the data processing stages, the algorithm used to create the models and why, and how the model outputs were implemented. You may be questioned about issues you had at any of these stages, so go over issues and challenges in previous projects and how you overcame them.
4. Last but not the least “Communicate Effectively”:
All the preparations in the world won’t help you if you can’t communicate effectively. Practice answering mock questions in your head. Focus on questions and business process which you might have come across in the past and have some answers to those so that you are not thinking too much on the fly at the actual interview. Of course, you can’t foresee every question, but if you spend some time articulating responses to specific questions, you’ll be better prepared with clear responses.
Conclusion:
A good analytics professional is a dynamic individual who understands both business and numbers and can provide insights using technical business tools.
To succeed in an analytics interview, you have to be adaptable. Do not be scared to showcase your skills to the company for which you are applying! Your interviewers will become your buddies by the end of the day. Simply relax and speak up. Listen more and have a good time 😉
