7 questions to ask at the end of a Data Science interview
Table of Contents
So you've made it through a 60-minute Data Science interview, and now the ball is in your court. Usually, at the end of an interview, the interviewer will give you a chance to ask them some questions.
Don't throw this opportunity away. Do not ask questions like "How is the free food here?" or "What is your favorite thing about working at this company?".
The importance of asking good questions
First and the last impressions stick — this is what people will remember of you when you leave. In an interview, this is usually the questions you ask at the end of an interview. This is your last chance to leave a good impression. Use it wisely.
Also, this is your chance to figure out if the company is a good fit for you. We often forget that an interview is a two-way street. While the company is evaluating if you're a good fit for them, you should be doing the same thing.
To help you in your next interview, here are 7 questions that you can ask as a Data Scientist. These questions will show that you know how data functions (and thrives) within a business. And will give you important insight on whether you want to work at that company.
By the way, you likely will only have time to ask 1 - 2 questions. So pick the questions based on what you're most curious about!
1. Who are the main stakeholders that I would work with in this role?
I like this question most because it demonstrates you understand that
Data Science is a collaborative function
Managing and building relationships with stakeholders is key
At the same time, it helps you gauge how cross-functional the role is and the kinds of communication you'll need to be effective. I find that knowing who your key partners are can tell you a lot about what your day-to-day will look like. For example, your role would be very different if your key partner is a Product Manager vs. a Sales leader.
Once you find out who your main stakeholders might be, you could follow up with these:
Are stakeholders generally data-savvy, or do they rely on you to interpret insights?
How much influence do stakeholders have over the projects the team takes on?
2. What stage of data maturity is the organization at — databases, reporting, and experimentation etc.?
Anyone who has worked in the industry knows this. The better the Data infrastructure, the more productive you are as a Data Scientist. Nothing is more frustrating than... querying raw event tables for every analysis or crunching numbers for every experiment.
You could dig deeper and ask who handles maintaining the data infrastructure. At some companies, Data Engineers are responsible for this; at others, Data Scientists have to do it themselves.
PS: If you're interested in infra, this can also be a great moment to express that interest & expertise.
3. Who does the Data team report into?
Why is this an important question to ask? Because your reporting line influences the priorities and expectations of the Data team.
For example,
A Data team embbeded in Sales might be more focused on building tools.
A Data team in Engineering could lean towards building more Data infrastructure.
A Data team in Product might spend more time on experimentation & exploratory analyses.
A centralized Data team (reporting into the CEO) often has more influence on company strategy.
I find it helpful to understand the org structure of a company, so you can anticipate the type of work you will do.
4. How is data used to drive decisions at the company? What's an example of how leadership made a strategic decision using data?
If an interviewer doesn't have a good answer for this, that is usually a red flag. As a Data Scientist, you want to work at a company that values data and knows how to use it.
Otherwise, what’s the point of being a Data Scientist?
One of my pet peeves is this. When leadership makes up their mind first, then asks for data to validate that decision they already made. So asking this question helps you sense whether data is actually driving strategy. Or if data is just used as an afterthought.
5. Who decides which projects the Data Science team works on?
Big strategic decisions at a company come from the top down or the bottom up. Top-down means that the leadership team sets the priorities and the team executes. Bottom-up means insights (including those from Data) help determine the company's direction.
I personally like to work on teams where there is some autonomy choosing what to pursue. Maybe I have a gut feeling or spot a pattern worth digging into — I want the freedom to explore it.
As a rule of thumb, I like to be able to set my own priorities between 20-50% of the time. But, not everyone is the same.
Figure out what your requirement for autonomy is. Then use this question to gauge if the company is a good fit for you.
6. What do you think is the most challenging part of being a Data Scientist here?
This question only makes sense if your interviewer is also a Data Scientist. I find this question illuminating. It gives you insight into the (harsh) reality of being a Data Scientist at that company.
I like asking this because:
It gives you a look at the day-to-day painpoints of the role
It shows you are genuinely interested in the role — you want the full picture
It gives the interviewer a chance to be real with you
This is an easy one to ask follow-up questions on. For example, you might ask about how they work around that challenge, or to understand why it continues to be a problem.
7. What is one problem you wish Data Science could solve here?
If you get a chance to interview with a key stakeholder, this is a great question to ask. It gives you insight into what is top-of-mind for them, and why they are excited to hire someone for this role.
It also reveals how much they understand the role of Data Science. One red flag is if they don't have a clear answer for this question. It'll make me question if they understand how to collaborate with a Data Scientist.
Bonus: this sets you up to hit the ground running when you start. If you get the job, you’ll already know what’s important to one of your main partners. This way you can start delivering value right away.
Remember, the last 5 minutes of your interview is your last impression. Don't waste that opportunity by asking generic, throwaway questions. Ask questions that help you decide if you want the job, and that shows them you know what being a Data Scientist means.