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Over the past ten years, technology adoption has accelerated to unprecedented levels of growth. For context, the first commercially manufactured automobile became available to the public in 1886 and by 1930 there were more than 23 million automobiles on the road - taking 34 years to reach what could be called "mass adoption." Only 79 years later, in 2009 Kickstarter was launched taking 30 months to reach 1 million users. Facebook then broke Kickstarter's record, taking only 10 months to accomplish the same goal.
Data Scientists have become a key part of this technological movement. Utilizing a combination of complex algorithms, machine learning, and artificial intelligence techniques, data scientists can construct predictive models for the data that are both accurate and reliable. Put simply, Data scientists unlock solutions from unstructured data by asking questions and employing scientific methods and algorithms.
When interviewing a data scientist, it is important to focus on the candidate's technical skills and understanding of data science fundamentals. Data scientist interview questions should cover topics such as databases, algorithms, testing models, datasets, and data cleaning, and specific lines of questioning can be sorted out through data science mock interviews with team leaders. It is also important to ask questions about the candidate's ability to interpret and communicate data in meaningful ways for decision-making.
If you're struggling to create questions for data science interviews, check out Pillar's interview intelligence tool. We have 1000+ SQL, R, Python, and other data science interview questions to ask candidates.
Obviously, the best way to test a data science candidate's technical skill is through assessment. Data science interview questions and answers show term familiarity and application knowledge, but don't fully encompass a candidate's working knowledge.
Vervoe offers a basic skills assessment test for data science candidates that covers python, machine learning, TensorFlow, and communications. Adaface, Codility, CodeSignal, and others offer data science exam questions and answers, PDF test results, and analytics that are designed to identify the most qualified candidates.
When it comes to data science interviews, be sure to ask questions specific to the candidate's experience and ability level. You're trying to find a great fit and it's important to look for more than just technical skills.
For example, entry-level data scientists should know their way around basic databases and algorithms while senior-level data scientists will need to understand advanced techniques such as artificial intelligence, machine learning, and predictive modeling.
At the end of the day, data science is a rapidly evolving field and it's essential to stay up-to-date on the latest trends, tools, and technologies. Utilizing a video interview platform combined with the proper assessment tools for your specific role with give you a great understanding of the candidate's technical skills and cultural fit.
Hiring a data scientist, especially your first one, can seem like an overwhelming challenge. Thankfully, there are plenty of data science interview questions and instructions out there to get you started.
For entry-level data scientists, start by describing the position and explaining why it’s important for your company. This will help set expectations right away. Keep in mind that a data scientist's core function is extracting knowledge from unstructured data - this will help you create scenarios and semi-structured questions that you can ask the candidate.
The next step is to actually ask the candidate questions to access their technical skill level. this can be done as early in the process as the screening phase with the help and validation of the tools mentioned in the previous section.
Entry-level data scientist interview questions should focus on topics such as database fundamentals, data cleaning and manipulation, SQL basics, and machine learning concepts.
Questions should also cover basic development skills like scripting languages (e.g., Python), web development frameworks (e.g., Flask, Django), and other tools that are relevant to the job position. As you hire mid-level data scientists you'll want to dive into topics like supervised and unsupervised learning, logistic regression, random forest models, and natural language processing.
Before progression onto soft skills, you may even want to create several data scientist scenario-based interview questions about the 3 types of differentiating variate analysis, and how they can be used to uncover hidden trends in data.
Don’t forget to test the candidate on their ability to explain and present technical information; it is a critical skill for any data scientist role. A great way to do this is by asking them to walk you through a data project that they have worked on before to assess their communication skills, especially with non-technical teammates.
Finally, it's important to remember that a candidate's technical skills are just one part of the equation - be sure to consider their soft skills as well, such as communication, collaboration, problem-solving ability, and adaptability.
Ultimately, you want to make sure the person you're hiring is someone who can work well with others, is passionate about data science and eager to learn, and is motivated by the challenge of problem-solving.
If you are looking to hire a senior data scientist, you’ll need to ask more in-depth questions about the candidate's technical skills and knowledge. This is because they will be expected to lead projects and mentor junior data scientists - so their skills must be up-to-date with the latest trends in the field.
Once you have evaluated your potential candidate's technical abilities, utilize panel interviews to investigate their communication, leadership, and mentorship capabilities as well as their capacity for forming successful teams that will help propel the growth of your business.
Senior data scientist technical interview questions should focus on advanced topics such as deep learning, neural networks, transfer learning, time series analysis, and supervised machine learning techniques.
Questions should also cover big data tools such as Hadoop, Spark, and MapReduce. You may even want to ask the candidate about their experience with various cloud technologies such as AWS (Amazon Web Services), Microsoft Dynamics, or Google Cloud Platform.
Finally, don’t forget to ask questions about data visualization, as this is a critical skill for any data scientist. Ask the candidate to explain how they would approach visualizing complex datasets and what tools they may utilize in order to do so. Additionally, you could assign them a mock project where they have to create a data visualization and present their findings to you.
By asking these questions, you can assess the candidate’s technical aptitude and gain insight into their problem-solving abilities. This will ensure that your company is hiring a senior data scientist who can not only deliver results but also mentor junior talent.
To start off this section, we're going to cover the 500 most important data science interview questions and answers... (actually I'm just kidding). Who has time for that? However, Pillar's video interview platform provides 1000+ questions that can be readily used as prompts during data science interviews. Consider this a "data science interview cheat sheet" to help you get the most out of each interview. This makes data science interview preparation much simpler and more efficient for interviewers, hiring managers, and recruitment professionals.
Since data science is one of the fastest-growing job fields in the world today, there is a variety of technical questions that could be asked. To begin, collaborate with your CTO (Chief Technology Officer) to craft questions that prioritize the qualifications and experience necessary for success in your open role. As you do so, try to tailor each question to the unique requirements of your organization. This will help narrow down the list of potential questions and make sure that you’re getting to the heart of what matters most.
Questions can range from high-level data science topics such as supervised machine learning, deep learning, and natural language processing to more specific questions about the candidate's coding capabilities and experience using particular software programs. Additionally, you can ask questions that assess a data scientist’s communication skills, problem-solving abilities, and leadership potential.
Remember - never be afraid to ask follow-up questions! This is your chance to learn more about the candidate's background and see if they have the technical expertise and experience to take your data science team to the next level.
If you're rethinking your hiring process, our team would love to help you make it better. We work with VC, tech, recruiting, and SaaS clients like High Alpha, Demandwell, Praxis, Terminal, and Talent Equity Group. Over the last 12 months, we've helped our customers lower employee turnover by 50%, shorten their time-to-hire, and create a more equitable hiring process for each candidate.
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