Data science is one of the fastest-growing fields in the tech, with many companies seeking to hire talented and experienced Data Scientists. If you're applying for a Data Scientist position, it's important to prepare for the interview process. As a Data Scientist, you'll be responsible for analyzing and interpreting complex data sets, identifying patterns and trends, and communicating insights to stakeholders.


In this post, we outline six common interview questions for a Data Scientist job, along with tips on how to prepare and answer them effectively. By preparing for these questions, you'll be better equipped to showcase your skills, experience, and knowledge to potential employers and increase your chances of landing your dream Data Scientist job.


Potential Interview Question 1: Can you walk me through a data science project you worked on from start to finish?


Think of a recent project you worked on where you were part of the team who identified a problem, developed a solution, executed your solution, and shared the outcome with a group of stakeholders.  


This can be a simple project – maybe you made the collection of data more efficient, or you improved an internal process. Or it could be a larger scale one – perhaps you were on a data science team who managed all the analytics and reporting for a huge e-commerce campaign, and reporting ontraffic lifts in particular markets and measuring brand awareness lifts (a traditionally hard-to-quantify metric) in a tangible way.


Whatever your project is, be sure to share what you learned and how you would apply your learnings to future, similar projects. Even if you failed, learning from your failures is important too.


Potential Interview Question 2: How do you handle missing or incomplete data in a dataset?


Handling missing or incomplete data is an important part of the data handling process. As a data scientist, you want to outline your thought process when asked this question so your interviewers can understand how you approach inevitable gaps.


You may wish to give an answer along the lines of, first you would identify which variables are missing and how many, and next determine the reason for missing data. After both these steps, you may want to choose a common method for handling missing data, like imputation (filling in the values with estimates), deletion (removing the missing values) or modeling (using statistical models or machine learning to predict the missing values).


If you can include a story here to explain how you handled missing data in the past – even better. Maybe you can share why the method you picked for handling the missing data was the best of all your available options at the time or share what you would do differently in the future.


Potential Interview Question 3: Can you describe your experience with Machine Learning algorithms?


Machine Learning has received quite a deal of press over the last several years, so you may be asked to detail your experience with Machine Learning in an interview for a data science role as well. If you’re asked about your experience with building or using algorithms, answer truthfully and share what your role on the team was. If you have experience with some of the languages or programs commonly used by Machine Learning Engineers, you may wish to explain this as well. These typically include Python, C++ or C sharp, MatLab and natural language processing.


Potential Interview Question 4: Can you describe your experience with measurement platforms or dashboards?

Many organizations rely on dashboards – basically, syncing their large data sets into dashboards used by data scientists (and non-Data Scientists) to discover new insights. You may be asked about your experience on configuring dashboards and which programs you’ve used, whether it’s Tableau, Mixpanel, Databox, Adobe product, or any number of products on the market available for large data sets.


Potential Interview Question 5: What’s your experience with distilling lengthy data reports into information that’s digestible for others?


Your interviewer will want to understand that you understand how to take large data sets and create succinct, easy-to-read, reports. This is of course, easier said than done! Sometimes, you may need to analyze hundreds or thousands of data sets just to arrive at one number or one prediction. If you can walk your interviewer through an example where you took an initially-intimidating ask and were able to figure out an answer, this will only aid you during the interview process.  


Related to this question, your interviewer may also ask some questions that evaluate how much you know about the particular industry you’re interviewing for. For example – if you’ve never worked at a B2B SaaS company before – will you understand the main metrics leaders at these companies care about, like churn rate or customer acquisition cost, if you’ve only worked in an industry that measured completely different data points? Knowing which data points to measure and care about is often as important as the validity of the data itself. Think through the data points your interviewer likely cares about before heading into your interview and you'll feel much more prepared to have a dialogue and ask questions.


Potential Interview Question 6: Can you describe a time when you had to present data to a large group and how that went?  


As for any job, soft-skills are important for Data Scientists too – and employers will want to know that you’re comfortable with presenting and sharing your findings.


Even if you shared your data with a small group, walk your interviewer through how you structured your presentation, what results you chose to share, and what action, if any, your presentation led the company to take. Did marketing choose a new, more successful strategy after evaluating the data behind some of their advertising spend? Did sales decide to pursue certain customers that have a higher LTV, after they discovered that the costs of acquiring other customers were much higher than they thought? Think back on conversations you had after sharing new findings, and write down some notes.


If you can share a few big picture insights that your data brought to light in one of your last roles – you’ll be well on your way to receiving an offer.