This article is a part of our in-depth Data Science Career Guide. To read the other articles, please refer to the table of contents or the links that follow this post.
Once your application materials are all squared away, it’s time to start thinking about the next stage in the application process: job interviews.
What If You’re Not Getting Job Interviews?
If you’ve got the technical skills needed for the roles to which you’re applying and you’ve done a good job preparing your application materials, eventually you’re going to start hearing back from employers interested in interviewing you.
Don’t be discouraged if this takes a while or if you have a low response rate. That’s quite common when applying to entry-level jobs. There’s a lot of competition for these positions, and the job hiring process can be fairly arbitrary. Stick with it!
If you’re applying to hundreds of jobs and not getting any interviews, though, there’s a good chance that something is wrong. It could be that you’re not qualified for the jobs you’re applying to, or it could be that something in your application materials is bothering recruiters. Either way, it might be worth consulting with someone knowledgeable who can assess your application and give you some idea of what’s going wrong. Dataquest Premium subscribers can get personalized career advice (including interview prep and portfolio and résumé reviews) from our trained Career Services community moderators. You can also ask friends or contacts in your data science network to help you figure out what’s going wrong.
Once you do start getting interviews, though, you’re going to want to be prepared for them. Your application materials have given you a shot, but the interview process is where you seal the deal and confirm to potential employers that you are the right person for the job.
What to Expect, and How to Prepare
Let’s start with the bad news: data science interview formats and questions are quite varied, which makes them difficult to prepare for.
“Honestly, the interview process varies so widely,” says SharpestMinds co-founder Edouard Harris. “Every company thinks they’ve found the light, and they have the one true way of interviewing people, so once you’ve made it to the interview, there’s a ton of variance in interview questions. You’ll get the IBM interview process which from what we’ve heard is really high level questions like, ’describe how a decision tree works. Describe how this works or that works.’ And then on the other hand there is Google, which is excruciating whiteboard coding sessions with engineers.”
“Some companies just steal the questions on Leetcode,” he says. “There are so many differences. There are virtually no standards in the industry. It’s a huge mess. Not enough companies really interview at scale for these roles to be able to collect meaningful amounts of data about whether their hiring process is effective. So interviews are just this voodoo ritual that nobody knows how to do.”
That’s the bad news. The good news is that even if you might not know exactly what to expect when you walk through the door, there are still lots of things you can do to prepare yourself to ace a data science job interview (and plenty of free job interview resources out there to help)!
What Hiring Managers Want
Although every interview is different, hiring managers and recruiters are typically looking to learn three main things about you during the interview process:
- How interested are you in the company and the role? They want to see that you’re actively interested in what the company does, and that you’ve already begun thinking about how you could bring value to the company in this role.
- How well does your skill set match the job’s requirements? They want to see that you’re technically capable of doing the job. Just because your resume says you know Python doesn’t mean you’re any good with it, so virtually every interview process will include some elements designed to test your technical skill. Employers also want to see you demonstrate important soft skills like communication.
- Would you be a good ‘culture fit’? They want to see that your personality works within their company culture, and that you’d be capable of working effectively and efficiently within their teams and systems. They also want to see that your personal career goals are aligned with the company and the role in question.
If you can leave the hiring manager and other team members feeling satisfied about all three of these things at the end of your interviews, your chances of getting a job offer are quite good.
Sample Interview Processes
Although every company approaches its data science job interviews differently, let’s take a look at some sample interview processes.
What follows are real interview processes for entry-level data analyst positions at a few different companies, based on interviewee reports on Glassdoor from the past few years, our own interviews with recruiters, and Dataquest students we’ve spoken with about their experiences. We’ve removed the company names because companies change their processes frequently and we don’t want to mislead anyone about exactly what they’ll be facing, and because we can’t always confirm the accuracy of what interviewees report. Reading about these processes should still give you an accurate idea of the different kinds of processes you may experience during your job search, though.
Major Tech Company #1, US Office: Two phone interviews, one technical with a data scientist, and one more focused on soft skills and culture with a hiring manager. Four onsite interviews focused on different aspects of the job (like technical skills, communication, etc.). The entire process took about three weeks.
Major Tech Company #2, India Office: An introductory screening round, and then two rounds of online testing focused on job-related technical skills, followed by a final personal interview round. The entire process took a couple weeks.
Major Tech Company #2, Singapore Office: One phone screening with the hiring manager, followed by three onsite interview rounds, mostly focused on technical skills and challenges. The entire process took about three weeks and the candidate called it “slightly exhausting.”
Startup #1, US Office:: Phone screening followed by a take-home project doing some data analysis and handing in a Jupyter Notebook file for the company to assess. This was then followed by two additional interviews, a technical interview and a final personal interview. The process took about three weeks.
Major Telecom Company, US Office: Two interviews (either phone or onsite depending on candidate’s location), one focused on personal questions and one focused on technical questions. Interviews were “relaxed” and short; the entire process took about one week.
Major Finance Company, US Office: A quick phone screening and then two one-on-one onsite interviews, with a surprisingly high focus on personal and behavioral questions rather than technical questions. The entire process took about two weeks.
Medium-Sized Data Firm, US Office: Technical screening by phone, followed by a more in-depth interview that’s focused on relevant work experience and solving technical challenges related to the company’s business. The third and final interview is more focused on personal and behavioral topics. The entire process took around three weeks.
Medium-Sized Software Company, US Office: Initial phone screening followed by a few rounds of onsite interviews that include both solving a work-related problem solo and working on real projects with the company’s team for most of a day. The process took a few weeks.
Major Tech Company #3, US Office: Screening conversations with the hiring manager and one of his employees, then two short interviews with employees in HR, and then an onsite interview loop that included five separate interviews. The entire process took more than a month.
As you can see, the experience varies a lot, but there are some common threads:
- You will have to answer technical questions or complete some coding-skills-related project at some point in the process.
- There’s often at least one interview that’s focused on soft skills and culture.
- Most hiring processes begin with a screening of some kind (often by phone) to weed out under-qualified candidates quickly.
Note, also, that it’s very possible you’ll be able to find specific information about the interview processes at the companies you have applied to online. Glassdoor is an excellent resource for this. Click on the “interviews” tab and then search for the company name. Once you’re on the company’s “Interviews” page you can filter for your job title in the lower search bar and then click “Find Interviews” to read about interviewee experiences associated with that job title.
This is an incredibly useful resource as long as you keep in mind a few caveats:
- These reports are submitted by users, so they could be inaccurate, biased, or outright untrue.
- Companies change their interview process frequently, so older reports may not reflect a company’s current hiring practices.
- Interview processes can vary from person to person: a candidate who’s been referred by a high-level employee, for example, may face a less rigorous and more informal process than a candidate who has no referral.
Unfortunately, the smaller the company you’ve applied to, the less likely you are to find helpful reports on Glassdoor. For many companies (including most startups) there’s a good chance you’ll find nothing at all.
Either way, though, don’t worry! You’re going to be prepared. Here’s how:
How to Prepare
We’ll get to specific sample questions you should study a bit later in this article, but first, let’s talk about general interview preparation. You should think about the interview process as being similar to an important test at school: if you walk into it without putting in the study time beforehand, you’re probably going to be in trouble.
Specifically, before every interview, you should review:
- The resume you submitted to this company. Be ready to answer questions about your background, work experience, skills — anything that’s mentioned on your resume could come up, and it doesn’t look good if an answer you give in an interview contradicts what you wrote on your resume.
- Your project portfolio. Particularly for entry-level roles, you should know your own projects inside and out. Be ready to answer questions about what you did, how you did it, why you did it that way, as well as broader questions about the programming and statistics concepts you’ve applied in your projects (interviewers want to see whether you just found some cool code to copy-paste from StackOverflow or whether you actually understand what’s happening under the hood).
- Technical questions related to the job description. There’s no way to be sure of precisely what technical questions you’ll be asked to answer or what problems you’ll be asked to solve in an interview, but if there are specific languages, techniques, or skills mentioned in the job description, you can almost certainly expect questions along those lines. Review what you know, being sure that you know not just how to do something, but also when and why you might want to do it. We have sample technical questions and links to more resources you can review a bit later in this article.
- Personal questions related to the job, your experience, and the job search. Don’t just assume you’ll be able to come up with a good answer for these questions off the cuff! Even though some answers seem obvious, it’s worth prepping answers for common job interview questions and questions you anticipate based on your work history before each interview.
- The questions you plan to ask in this interview. For each interview, you should prepare at least 3-5 questions you can ask the interviewer if given a chance. We’ll discuss this in more detail later in this article, but preparing good questions to ask means doing some research and doing some real thinking about what your role at this company would be.
Writing down outlines for your answers is a good idea, but it helps to practice actually speaking them out loud, too. Compile a list of “mock interview” questions and ask a friend or family member to help you prep. Even if they don’t know anything about data science, you are likely to realize if there are technical concepts you still need to brush up on, and they will still be able to offer useful feedback on the “soft” aspects of the interview, like how confident you sounded and how convincing your answers to the personal questions were.
Recording yourself on video answering mock interview questions is also a helpful (and sometimes painful) exercise. Set your phone down somewhere where it captures your entire body and then record yourself responding to different interview questions. You may be surprised by what you find!
What to Wear, How to Present Yourself: Pre-Interview Prep
Before we dive into sample questions, there’s one other aspect of data science job interview preparation that we need to cover: presenting yourself. Like it or not, this matters; how you present yourself affects the first impression interviewers get upon meeting you (whether it’s in person or via video chat), and first impressions are important.
In fact, it’s a little scary how important first impressions are. Some studies suggest that people make important, hard-to-change judgments about you within the first two seconds. It’s very important to know your stuff going into a data science job interview, but it’s arguably just as important that you’re presenting yourself well.
So what does that mean?
Clothing: You should wear clothing that is clean and that is appropriate for whatever workplace you’re interviewing in. It varies by industry; at a tech company, you’d likely be fine with casual wear, but for a finance interview you may need a suit.
If you’re not sure about the company’s general dress practice, it’s totally okay to ask about this before the interview. When in doubt, err on the side of caution. It’s definitely better to feel a little overdressed than it is to show up in flip-flops and shorts and discover that everyone else is wearing suits.
Grooming: You need to look professional. That can mean all sorts of things to all sorts of people, and to some extent, it varies by industry. But in general, you probably want your hair to be neat (and away from your face). You want clean and trimmed fingernails. Et cetera.
Hygiene: This, too, is pretty straightforward: you shouldn’t smell bad or appear to be unclean. It’s probably a good idea to be sure you’ve showered and brushed your teeth before an interview. Having a few mints on hand to keep your breath fresh never hurts, either.
Additional considerations for video interviews: If you’re doing a video interview rather than an on-site interview, give some thought to what your interviewer will be seeing. Here are some things to consider:
- What’s the background? A blank wall is fine, a clean and well-organized room is fine, wall art is fine as long as it looks reasonably professional. What you don’t want in the background are things like an unmade bed, a pile of dirty clothes, or a bunch of weird paintings of skulls.
- What are you using for the chat? If at all possible, use a computer, webcam, or phone that’s been placed somewhere stable. Holding a phone in your hand or chatting with your computer on your lap can make the video look very shaky for the interviewer.
- What do you look like? Try to set up your computer or camera at roughly eye level, so that you’re looking directly into it rather than down on it or up at it. This ensures that your interviewer will see you at a normal angle. Consider the lighting, too—your face should be clearly and evenly lit. Don’t be afraid to bring in a lamp or two if you need it to make sure your face is well lit!
- How does your equipment work? Test everything with a friend in advance to make sure they can hear and see you clearly and there are no unforeseen technical issues.
- Where are you looking? Video chats can be difficult in this regard, because if you look someone in the eye as it appears on your screen, they will usually see you looking below their eyeline, since most webcams sit on the top of the screen. If you can, try to remember to look at your camera rather than your screen while you’re speaking. This will make it appear to the interviewer like you’re looking them in the eye. (But if you find this too difficult, don’t worry too much about it — giving good answers is more important, and most interviewers will understand that it’s tough to look someone “in the eye” during a video chat).
Answering Interview Questions
Now that you’re all prepped, it’s time to talk about the actual experience of the interview, take a look at some of the questions you’re likely to be facing, and discuss a crucial aspect of the interviewee’s role: listening.
The Importance of Listening
“Just being a good listener” can really help in a job interview, says Data Mining Cookbook author and OliviaGroup CEO Olivia Parr-Rud. “In fact, listening and then maybe even repeating back to the person to say, ‘Okay, just want to make sure I understand what you’re looking for,’ and then maybe take it a little further so that they feel heard, is huge.”
“It’s amazing to me how many people don’t feel heard, and how just the act of listening can really connect you,” she says. “It would, to me, give a huge advantage, even on an unconscious level, to somebody who’s in an interview.”
There’s some real science behind Parr-Rud’s recommendation that you listen and then repeat back the question. We discussed this topic in more depth in a recent blog post, but the takeaway is this: study after study has confirmed that simply repeating a person’s question verbatim tends to give them a better impression of you and produces better results. In various studies, simply listening and repeating has increased sales numbers for salespeople, tips hauls for waitresses, and even donations to charity.
You don’t have to restate every question verbatim, of course. And you still need to know how to answer them correctly. But a simple exchange like this can, the science suggests, be surprisingly powerful:
Interviewer: Could you explain a decision tree algorithm?
You: OK, here’s how I would explain a decision tree algorithm…
Restating the question with the interviewer’s own phrasing (or something similar to it) demonstrates that you’re listening actively to what they’re saying. It could be helpful for you, too — it forces you to think about the question you’re answering and gives you a little bit of time to think about what you’d like to say.
So although your answers to questions are crucially important, remember that listening is quite important, too.
A Note About Answering Questions
When answering any interview question, you should have three goals in mind:
- Be clear.
- Be concise.
- Answer appropriately for your audience.
Mastering the first, be clear, is mostly about preparation. You can only explain something clearly when you know what you’re talking about.
Mastering the second, be concise, is about carefully listening to and answering the question, but not more. Avoid getting into minute details or going off on tangents and give direct answers to questions as they’re asked, offering more detail if it’s desired. If you’re curious about why, this article offers some great insight, but the short version is that offering too much detail can be off-putting and undermine your credibility.
You can say that you’d be happy to go into more detail if the interviewer would like, and you can also ask an interviewer to specify how in-depth they’d like your answer to be. But when in doubt, keep it relatively simple. “Why did you choose this algorithm?” should prompt a 1-2 minute answer about what the algorithm does and why that’s useful for your purposes, not a 20 minute lecture on its every intricacy and every possible application.
Mastering the third, answer appropriately for your audience, just requires you keep in mind who you’re talking to. Are you speaking to a data scientist with 15 years of experience? Get as technical and use as much jargon as you want! Are you speaking to an HR rep who hires for every team at this company? You may need to give bigger-picture answers and explain-like-I’m-five some of the more complex technical stuff if it comes up. You’ll also want to avoid using jargon like “data munging” — instead say something like “I cleaned up the data,” that anyone, regardless of their programming background, can probably understand.
Sample Questions About Your Projects
If you don’t have much work experience, you should expect to be asked about some or all of the projects you’ve showcased on your resume, in your application, and on your GitHub. Here are some sample questions you can run through for each project to be sure you’ve got good answers:
- Why did you choose to do a project about this?
- What does this project mean to you?
- What was your favorite thing about working on this project?
- What was your least favorite thing about working on this project?
- What technical challenges did you face during this project and how did you overcome them?
- Where did you get this data set, and what techniques did you use to clean the data?
- Why did you choose to use the statistical techniques you used for this project?
- Why did you choose to use the programming techniques you used for this project?
- Could you explain how this algorithm/statistical technique/section of code works?
- What libraries, packages, or other tools did you use for this project?
- How long did it take you to put this project together?
- If asked to, how might you expand on this project?
- If you had to do it again, what might you change about this project?
- How will the skills you used on this project be valuable to our business?
- (If group project) What was your job on this project?
- (If group project) How was this project organized and version-controlled?
- (If group project) Can you talk about a conflict or disagreement you had with teammates during this project and you overcame it?
Depending on what’s in your project, you might be asked other specific questions about the programming challenges, the data, and the statistical approach you took in your analysis. Beyond just being able to answer the questions above, you should review all of your projects to be sure you understand what your own code is doing, and that you can can clearly explain why you made all of the decisions you made.
Sample Technical Questions
The technical questions you face in a job interview are going to vary a lot based on the role you’re applying for, the company you’re applying to, and random chance. If it’s any consolation, the technical questions might not be as important as you think — when we asked, most of the recruiters and hiring managers we spoke to said that personal questions and questions about projects typically offered them more insight into whether a candidate was “right” or not.
But of course, that doesn’t mean you’ll get offered a job if you answer all the technical questions wrong!
Below, we’ve listed some sample technical questions you might face for data analyst and data scientist positions, but it varies a lot. What we have here is just a tiny sample of some of the possibilities, so below this list we’ve also linked to more resources where you can find many more practice questions.
- Explain the data analysis process.
- Why is data cleaning important?
- What kinds of problems would you look for when cleaning a data set?
- How would you get a data table from a web page into your code for analysis?
- How would you combine these two tables using SQL/Python/R?
- How would you estimate the number of windows in San Francisco? (There is an infinite variety of “logic” questions like this that are meant to test your logic and statistics skills).
- How would you sort the rows of this table numerically using SQL/Python/R?
- What kind of data would you want to collect to solve a specific business problem?
- What methods would you use to analyze the comparative performance of two different product search engines?
- In SQL, what’s the difference between Union vs. Union All? Union vs Join? Having vs Where?
- Explain random sampling, stratified sampling, and cluster sampling.
- Talk about a time you’ve worked with a large database or data set
- What are Z-scores and how are they useful?
- What would you do to analyze the best way for us to improve conversion rates for our users?
- What’s the best way to visualize this data and how would you do that using Python/R?
- If you were going to analyze our user engagement, what data would you collect and how would you analyze it?
- What’s the difference between structured and unstructured data?
- What is a p-value?
- How do you handle missing values in a data set?
- If an important metric for our company stopped appearing in our data source, how would you investigate the causes?
- How do you select features for a model? What do you look for?
- What’s the difference between logistic regression and linear regression?
- Explain decision trees.
- How would you test whether a new credit risk scoring model works?
- Explain K-means clustering and when it’s useful.
- If you have more than one trained model, how do you assess which is best?
- Explain the bias-variance tradeoff and how you navigate it.
- What is the Central Limit Theorem?
- What are the assumptions of a linear model (or any other type of model)?
- What’s the difference between K Nearest Neighbor and K-means Clustering?
- How do you address overfitting?
- Explain Naive Bayes algorithms.
- How do you find and correct biases in your data?
- What is cross validation?
- What are confounding variables?
Technical Question Resources
- Glassdoor has user-reported real-world interview questions and suggested answers for thousands of companies, searchable by job title. (This link goes to their data analyst questions, but you can search for any job title).
- Leetcode has a ton of practice SQL questions, organized by difficulty, in its “database” sections.
- Data Science Interview is a big and free collection of data science interview questions and answers, some technical and some not, on Quora.
- The DS Interview has hundreds of real interview questions, sortable by difficulty.
- Dataquest courses end each mission with downloadable PDF takeaways summarizing key concepts and they can be an invaluable resource for quizzing yourself on everything from Python to SQL to machine learning and much more, depending on which classes you have completed.
- Acing AI Interviews posts regular articles with data science interview questions from big companies like Quora, Oracle, Twitch, Yelp, and Spotify.
- HackerRank hosts a bunch of coding challenges you can work through if you sign up for an account, some of which could be similar to problems you get in an interview.
- Codewars allows you to test your skills with Python and SQL challenges (sadly, there’s no R).
Sample Personal Questions
- Talk about your a professional failure you experienced and how you overcame it.
- Why do you want to work at this company?
- Where do you see yourself in five years?
- What’s your career plan and how does this company fit into it?
- What impact on our company do you see yourself having in this role?
- Talk about your passion for data science and why it interests you.
- What kind of data do you think we should be collecting and analyzing?
- (If you don’t have a formal education in data science) Can you talk about how and why you learned data science?
- Talk about how you stay up to data with developments in the data science field and what trends on the horizon excite you.
- What are your greatest strengths and/or weaknesses?
- Why are you changing careers/leaving your previous job?
- Talk about your ideal work environment.
- Talk about a time you had a disagreement at work and how you resolved it.
- What’s your proudest accomplishment in data science so far?
- What data scientists do you admire most?
- What about this job posting appealed to you?
The “Expected Salary” Question
One interview question deserves special mention here: “What is your current salary?” or “What is your expected salary for this position?” These questions typically come towards the end of the interview process, when the company is thinking about whether or not to make an offer, and they can be very challenging to answer.
Generally, though, you should try to avoid providing your current salary. Asking for this is actually illegal in some US states, but even if the question is legal where you live, it’s best to politely dodge it. Saying something like “I’m not comfortable disclosing my current salary, but here’s the salary range I’m expecting based on my experience,” should be fine.
The key, of course, is that you need to know a salary range that is reasonable given the job description, your skills and experience, and the local market. We’ll go into a bit more depth about this—knowing your market value—in our chapter on offer negotiation, but the short version is this:
- Do some research on sites like Glassdoor to find out what people in similar roles in your local area are paid.
- Give a range rather than a specific number; this gives you some room to negotiate further down the road.
If you’re asked this question early in the interview process and haven’t done the research yet, it may be best to simply deflect it entirely with something like: “I’m looking forward to discussing compensation once I’ve learned more about the position.”
Questions You Should Ask
The interview process isn’t all about answering questions, it’s also about asking them. Most interviewers will end each interview by giving you an opportunity to ask questions, and you should not pass it up. This is a valuable chance for you to learn more about the company and to further impress the person you’re speaking with.
Ask to Impress
Most of the recruiters and hiring managers we spoke with for this guide agreed that their impression of a candidate was influenced by the questions they asked, and that asking the right questions could help a candidate. Among the types of questions they recommend asking:
Detailed questions about doing the job: The key here is detailed; don’t just ask what you’ll be doing day to day. Ask specific questions that show you’re already thinking about how you would function most effectively in the position, like questions about specific tools or workflows you’ll be using or specific business questions you’ll be addressing. For example, you might ask about a company’s flexibility towards using a tool or package/library that isn’t included in their tech stack, but that you think might be helpful in the role you’re applying for.
“I’ll get questions like, ‘oh have you tried this? Do you work with this type of data? I’d imagine you do this or that,’” says G2 Crowd Data Science and Analytics Manager Michael Hupp. “And we haven’t gone down that road, but just the fact that they’re thinking about that really stands out.”
Detailed questions about the business problems you’ll be solving in the position: As above, the idea behind asking this kind of question is to demonstrate that you’re already mentally engaged with solving this company’s business problems.
“The questions that really stick out as good are ones that demonstrate that they have done their homework and are interested in Kitware specifically,” says Kitware HR Director Jeff Hall.
Outlier.ai CTO Mike Kim agrees: “The questions I can think of that stand out as, ‘Wow, these were impressive questions,’ are the ones that really demonstrated that the candidate understood our problem, what they were working on, what we are working on, and how they fit into that.”
This is a sentiment that was echoed by many of the other recruiters and hiring managers we spoke with for this guide.
This technique is particularly helpful when interviewing at smaller companies, Edouard Harris says. “Almost always the best question to ask is: ‘What’s the biggest bottleneck for you at the moment? What is the one thing that’s blocking you as a company the most?’ What that question does is tell the interviewer: this person, right out of the gate, wants to know how they can be as helpful as possible.”
That particular question is also a valuable information-gathering tool, Edouard says. “If the company is 20 people or less, virtually everyone in the company should be able to answer that question. If not, there’s some kind of communication problem, which is itself a red flag.”
Questions about growth opportunities and training: These questions demonstrate that you’re interested in continually improving your skills and learning, which is something most employers want to see. (And of course, it’s also valuable information for you to have later when you’re assessing offers; a company with a lower salary offer could still be the better choice if it can also offer great training opportunities that’ll be better for your career in the long term).
Questions about collaboration with other departments: Data science teams typically have to work in collaboration with a lot of other departments. Questions along these lines show you’re interested in that aspect of the position, and the answer will probably give you some idea of what the company’s culture is like, and how efficient the collaborative workflow is likely to be.
Questions about long-term plans and projects you’ll be working on: “Those are the questions that I look for,” says CiBo Technologies Talent Acquisition Manager Jamieson Vazquez, “folks that want to know what the long-term future is, want to know where we are building but want to know how they can really impact those future plans too.”
What Not to Ask
Asking no questions at all: This demonstrates to an interviewer that you’re not engaged at all. You should always have at least a few questions ready.
Asking about compensation, paid time off, etc.: The appropriate time for these kinds of negotiations is at the end of the interview process, after you’ve received a job offer. If you ask about this before then, especially if you ask about it repeatedly, interviewers will get the impression that you’re just in it for the paycheck and not genuinely interested in the work.
Asking questions with easy-to-find answers: If you ask about something that’s clearly answered on the company’s website, for example, it just shows the interviewer that you haven’t bothered to do your research prior to the interview, and that suggests you don’t really care about the job.
The Bottom Line
Your questions need to show that you’re actively thinking about the ways you can help this company from this role, and they need to demonstrate that you’ve done your homework when it comes to the company’s business. They need to be specific to the company you’re interviewing with; there’s no cheat-sheet list of questions that you can use in each interview and still make a good impression.
“What really separates the best from the rest,” says Mike Kim, “is the depth to which they go with their questions. And I don’t mean nitty-gritty technical questions. I mean questions that show that they see the foundations for what they are, and understand how things connect. That’s really what’s impressive.”
That means that prior to the interview, you need to spend some real time studying the company and its business, and thinking about the ways that your role can impact it.
Following-up After the Interview
During the interview, you may or may not have been given some kind of timeline for the next steps in the process. Waiting and watching your email can be agony when you’re hoping to hear back about a job offer or the next stage of the interview process — especially if you were told you’d hear back by Monday, and it’s already Tuesday! So when should you reach out and touch base after an interview?
First, it can be a good idea to send the interviewer a very brief email message the day after your interview. The purpose of this message, though, should just be to thank them for their time and reiterate your interest. It could be something like:
Thanks so much for taking the time to speak with me yesterday about doing data science at [Company]. I really enjoyed meeting the team, and I’m excited by the prospect of working on [specific business problem related to the job]. Please let me know if there’s anything else I can provide to assist you in assessing my candidacy. I look forward to hearing from you,
After that, you should sit back and wait until a reasonable amount of time has passed before contacting the company again.
If you were told you’d hear back by a specific time (i.e. “You should hear back from us about next steps sometime next week.”), you should wait at least at two business days after that deadline before getting in touch. And you shouldn’t panic—it’s common for things to get pushed back a couple of days.
If you weren’t given a specific deadline, you should wait at least a week (five business days) before reaching out again.
Either way, this message should be similar to the previous one: short, friendly, and eager but not impatient. It’s also good to end with a question (that’s more likely to prompt a response), but you should make sure that your question is offering something rather than demanding something (“Is there any additional information I can provide?” is better than “When can I expect to hear back?”).
Consider a message like:
Thank you again for your time last week! I just wanted to reach out to reaffirm my enthusiasm for this position. I’m confident I can bring a lot of value to [Company] and I’m eager for a chance to prove it. I look forward to hearing about next steps. Is there any additional information I can provide to help move my application forward?
After sending a follow-up message like the above, wait another week. If you still haven’t heard anything back, it’s probably best to assume you haven’t gotten the position. Don’t burn any bridges — sometimes companies get behind or something gets lost in the shuffle, so you still might end up getting another interview or an offer — but it may be best to refocus your time and energy on applications with other companies.
Learning from rejections
If you do hear back with a rejection, it’s important not to lose heart. Remember, the fact that you got an interview in the first place means that you’re doing something right, and the company saw something they liked in your application materials. More interviews will come.
It’s also important that you see rejection as an opportunity for growth. Reflecting on your own performance can be helpful. Were there any questions you struggled with in the interview? Figure out how to answer them better next time.
It’s also okay to ask your interviewer for some insight. They don’t owe you a response (and you won’t always get one), but if you politely reply to a rejection message with something like this, you’ll often get valuable feedback you can use to make sure the next interview goes better:
Thank you for letting me know, and for taking the time to consider my application. I’m always looking to improve, so I’m wondering: what do you think I should work on to make myself a stronger candidate next time?
If you do hear back, your reply should be a simple “Thank you!” or something to that effect — _do not push back or argue, and do not try to convince a hiring manager that they made the wrong decision (this will never work).
Don’t be offended if you don’t hear back. Some companies have HR policies that forbid giving this kind of feedback.
Getting the Good News After an Interview
When you hear good news after an interview (for example, being told you’ll be getting a job offer), you’re bound to be excited. But it’s worth keeping in mind the old adage: “It ain’t over ‘till it’s over.” A verbal promise that you’ll be getting an offer doesn’t necessarily mean you’ll really get one. Something could go wrong financially at the company, or the interviewer could have spoken out of turn about a decision they can’t make by themselves.
These situations are uncommon (if you’re told you’re getting an offer, you’re almost certainly getting an offer), but it’s still wise to wait until the ink is on the contract before taking major steps like withdrawing your other job applications. Celebrate your victory, of course, but keep your options open until you’ve officially accepted an offer (and don’t accept an offer without negotiating; see our offer negotiations chapter for more details).
This article is part of our in-depth Data Science Career Guide.
- Introduction and Table of Contents
- Before You Apply: Considering Your Options
- How and Where to Find Data Science Jobs
- How to Write a Data Science Resume
- How to Create a Data Science Project Portfolio
- How to Fill in Application Forms, When to Apply, and Other Considerations
- Preparing for Job Interviews in Data Science — You are here.
- Assessing and Negotiating Job Offers