As a data analyst or data scientist, we not only need to know probabilities and statistics, machine learning algorithms, coding, but most importantly we need to know how to use these techniques to solve any business problems.
As a data analyst or data scientist, we not only need to know probabilities and statistics, machine learning algorithms, coding, but most importantly we need to know how to use these techniques to solve any business problems. Most of the time, you will be given a 30–45 min interview with a single data scientist or a hiring manager in which you’ll answer a multifaceted business problem that’s likely related to the organization’s daily work.
When I first started to prepare for the case study interview, I didn’t know there are different types of case studies. The fastest way to be an expert in the case study is to know all the frameworks to solve different kinds of case studies. A case study interview can help the interviewers evaluate if a candidate would be a good fit for the position. Sometimes, they might even ask you a question that they actually encountered. Understanding what the interviewers are looking for can help you better prepare for your answer.
📌 Logical and actionable thinking process
▶️ The interviewers care about your thought process and how you get the solution. If you are able to get the answer without any framework, the interviews might think this might just be your lucky day, and you wouldn’t be able to solve a problem next time.
▶️ Sometimes, if you are heading in the wrong direction, the interviewers might throw you some questions to help you get back on track. If this happens during your interview, you were likely missing something they felt was important.
▶️ It’s normal to be nervous during a job interview. However, as an analyst, if you don’t believe in yourself, how can others believe in you?
📌 Clear communication
▶️ As an analyst, the ability to analyze data and interpret your results is very important. Depending on your company, some analysts do need to present their analysis to the stakeholders.
They want to know if you can take a systematic approach to problem-solving, and that you can describe it clearly. For example, they might ask you: “This month the active user accounts have increased by xx%, can you tell us what’s going on?”
To answer this type of question, you don’t have to have an answer ready right away. What matters the most is your thought process. Don’t tell them any of your guess or assumption immediately. Answer it with a framework. Ask them questions to show that you understand the business.
They want to know if your answers cover all aspects of the problem. For example, they might give you a situation and ask you questions like “How does it look,” “What is the current problem,” “How can we improve,” etc.
There are many ways to answer these types of questions. The key to getting the perfect answer is to be concise and provide a recommendation at the end. Let’s say the Vice-president (VP) of Marketing asks you “How does it look?” in 2 mins, you can answer it by stating what the current goal is, where we are at, how far away we are to the company’s goal, what we are planning to do next to reach the goal (suggestions), and how long it would take for us to get there.
They want to see if your answer is practical and could realistically be implemented. Be more precise; use numbers when possible.
I’ve noticed that a lot of analysts often forget to use numbers to support or back up their points. If you want to let the manager know it’s not possible to reach specific goals based on your calculation, you should use the numbers from your analysis to prove why it. Using numbers can help your suggestions be more convincing and reliable.
Sometimes, they might ask you to answer a vague question such as “How does it look,” to different people (your manager, the CEO, etc.). This is similar to when a recruiter asks, “Tell me about yourself,” you don’t want to get down to the nitty-gritty of the projects you’ve worked on because your recruiter may be losing interest. On the other hand, the hiring manager might expect you to talk more about your past projects.
This also applies to a case study interview. Knowing who your audience is is significant. If you are talking to the Vice-president (VP) of Sales, he or she might care more about the number of customers. On the contrary, if you are talking to VP of Marketing, he or she might not care about the number of prospects as much as the VP of Sales, and instead, they might care about how to increase response rates and conversion rates.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Based on my experience both as a candidate and a hiring manager I address some typical questions around data science case studies.
Analytics India Magazine brings the list of leading analytics and data science products for the year 2020 that have positively impacted businesses across the globe, helping them make decisions. To source the best 10 products, we reached out to more than 25 companies. Ranging from serving financial sectors to manufacturing, retail, solar and other industries,…
Data is top of mind for most product managers. Models Will Run the World, with the big winners being those model-driven organizations that build products that collect data.
Managing Data Science as Products. How data science teams can apply product management practices to solve their biggest challenges