When interviewing for a job, data scientists need to consider how to showcase both their technical acumen and soft skills to potential employers. Data science interview prep should start by considering the target company, the kinds of questions they might ask and how to highlight your appropriate strengths.
The best data scientists build effective models, use appropriate techniques for different kinds of problems and strategize about augmenting data sets. Data scientists must also be excellent communicators with business intuition, have a boardroom presence, and be able to build strong teams to support them.
"It is essential to understand the role and what is expected of data scientists before the interviewing process begins," said Vivek Ravisankar, CEO at HackerRank.
Maintaining clean, extensive data sets is the biggest challenge in many data science projects. Sachin Gupta, CEO of HackerEarth, recommends data scientists review their strengths in four key skills: programming, statistics and probability, machine learning and data analysis. Interviewers will commonly ask questions on these topics, such as the following:
Gupta recommends practicing programming questions across different topics. It's also helpful to practice system design questions and coding during pair interviews with your peers.
"That way you will be more confident during the final interview," he said.
Potential data scientists should also consider refining their soft skills since data scientists are required to work cross-functionally across organizations and must know how to communicate with their colleagues. This will not only help candidates shine in the interview process but will set them up for success in the role itself.
Another aspect of data scientist interview prep consists of understanding the potential employer.
Magda Klimkiewicz, an HR business partner at Bold LLC, recommends checking the About Us page and reading up on the prospective company on Glassdoor before the interview. Be sure to read through the organization's core values, vision and mission statements to get a taste of what the employer might be looking for in candidates.
"If you can prove to employers you understand their business and their coding needs, they'll throw job offers at your feet," she said.
Before the interview process begins, you should look at specific questions you might be expected to answer in preparation of the technical screening process. Jen Hsin, head of data science at SetSail, suggests reading through the job description to identify which data scientist profile the hiring team is looking for.
Some of the possible profiles may include descriptions similar to these:
Jennifer Raimone, director of career and student support for Metis, recommends getting into the habit of finding out more about the company during the initial phone screen.
"It's surprising how many job seekers are afraid to ask what the interview process entails, but understandably so since we aren't really taught how to navigate this process," she said.
Here are some good questions to ask during the initial phone screen:
Asking about their timeline helps you schedule your time better so you are not overworked and can be your best self. If the position is backfilled, you can think about what skills to highlight. Asking the steps in the interview process helps you prepare technically, and understanding their communication style can help you to manage your expectations.
Sean Downes, Ph.D. director at the Pasayten Institute, recommends brainstorming the kinds of problems that the organization might face and charting out possible concrete problems with concrete solutions. For example, a social networking company might be seeking ways to curate the best clusters on a graph; a retail company might want help setting up or improving a recommendation system.
Part of your data science interview prep could include writing this up in a chart to use as a formula sheet on a phone call to remind you of any important details that might otherwise be lost in the heat of the interview. It may also be helpful to create a map with related problems you have worked on that includes details for the packages or methods you used, what worked, and what did not.
For the technical interview, it's good to brush up on your ability to write complex SQL queries and prepare for simple data processing-oriented coding exercises that involve writing a script, said Rudy Zen, head of product at Fast AF Inc. These exercises are meant to mirror the technical skills needed for a day in the life of a data scientist.
Sample SQL questions could include how you would handle data aggregations by group, windowing functions such as ranking, complex joins and subqueries. Consider looking at sample SQL exercises and going through them; this can be especially effective to prepare if you aren't using SQL regularly.
Paul Bilodeau, CEO and co-founder of Filtered.ai, recommends finding people that work at the company you are interviewing for on LinkedIn. Then, take a look at what they have listed as the projects they're working on and the current tools they're working with.
You might also want to explore what they are posting on Twitter and GitHub, which can help you know what topic you want to review.
It's also worth considering the kinds of problems the interviewer might be working on. For example, if you are interviewing for a position that skews toward data analysis, know the difference between basic distributions cold, and know what they're used for. If you're interviewing for a position that skews toward machine learning, be prepared to explain what models you've used and be prepared to talk about problems with deployment.
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