Whether they are experts or recent graduates, during a job interview, data scientists should think about how to showcase both their technical acumen and their generic skills to potential employers. Preparing for an interview in this field should begin with an examination of the target company, the types of questions you might ask, and exploring ways to highlight your strengths.
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The best data scientists build effective models, use appropriate techniques for different types of problems, and develop strategies to augment data sets. They also need to be excellent communicators with business intuition, have a presence on the board of directors and be able to build strong teams to support them.
“Understanding the role of data scientists and what is expected of them before the interview process begins is essential,” says Vivek Ravisankar, CEO of HackerRank.
Keeping large data sets clean is the number one priority for many data science projects. Sachin Gupta, CEO of HackerEarth, recommends that data scientists review their strengths in four core skills: programming, statistics, and probability, machine learning and data analysis. Recruiters will typically ask questions about these topics, such as the following:
- Programming: Given a set of unsorted random numbers (decimals), find the distance between the interquartiles.
- Statistics and Probabilities: Let’s say you roll three dice, one at a time. What is the probability that you will get three numbers in strict ascending order?
- Machine Learning: Let’s say you have a categorical variable with thousands of distinct values. How do you write it?
- Data analysis: From a table about user actions, write a query to see how well they are retaining monthly active users.
Sachin Gupta recommends practicing answering programming questions on different topics. It is also helpful to prepare for system design and coding tests in pair interviews with your peers.
“This will give you more confidence in the final interview,” he adds.
Future data scientists should also consider improving their general skills, as they are called to work casually within organizations and must know how to communicate with colleagues. This will not only help the candidates shine at the interview, but also help prepare for the job itself.
before the interview
Another aspect necessary to apply for a position as a data scientist. Before the interview, you should understand the motivations of the hiring company.
Magda Klimkiewicz, HR Partner at Bold LLC, recommends visiting the About Us page on the relevant company’s website and checking Glassdoor prior to the interview. Be sure to read the organization’s core values, vision, and mission statements to get a glimpse of what the employer might be looking for in candidates.
“If you can show employers that you understand their business and their programming needs, they will be under your feet to provide you with offers,” she adds.
Before you begin the interview process, you should think about the specific questions you may need to answer to prepare for the technical selection process. Jen Hsin, Head of Data Science at SetSail, suggests reading the job description to determine the specific profile the team wants to set.
Some potential profiles may include descriptions similar to the following:
- statistician/researcher. Defined roles and responsibilities can include A/B testing, hypothesis testing, user research, etc. In this case, you may be asked to prepare a design of experiments, estimate sample size, calculate a confidence interval, and when to accept or reject a hypothesis based on statistics.
- ML world. This job description could be about creating machine learning models to solve a business problem or improve product properties. You may be asked to talk about your past experience with machine learning: What problem did you try to solve using machine learning? Which Algorithms Was it evaluated and used? Why is this algorithm suitable for the problem compared to other options? How do you measure the performance of the model? What is the effect of the model in real life?
- Specialized in Neuro Linguistic Programming or Computer Vision. Relevant job posting authors specifically claim expertise in word processing or image processing. In this case, familiarity with modern deep learning methods (deep learning) essential. The questions could be: What new developments are you particularly excited about? What new algorithm has just been released that you are about to try? What do you think are the most efficient neural networks today? why ? Can we spread it in business?
Get ready for the interview
Jennifer Raymond, director of career counseling and student support at Metis, recommends getting used to learning more about the company during your first phone interview.
“It’s surprising that a lot of job seekers are afraid to learn more about the interview process, but that’s understandable, because we haven’t really learned how to navigate the process,” he says.
Here are some good questions to ask during your first phone interview, regardless of the nature of the job:
- What is the deadline for filling the position?
- Is this a new position or an alternative?
- What is the interview process?
- What is your preferred communication style for follow up and updates?
Asking for a temporary schedule will help you plan your time better so that you are not overwhelmed and can perform at your best. If the position is filled, you can think about the skills to highlight. Knowing the steps of the interview process helps you prepare technically, and understanding recruiters’ communication style can help you manage your expectations.
Shaun Downs, director of the Basaitain Institute, recommends brainstorming the kinds of challenges an organization might face and trying to find concrete solutions to specific issues. For example, a social media company might be looking for ways to select the best groups on the chart. The e-commerce company may request assistance in publishing or improving the referral system.
As part of your preparation for this interview, you can write a chart that you will use as a formula board in a phone call to remind yourself of any important details that might be lost in the heat of the conversation. It can also be helpful to create a map of relevant issues you’ve worked on detailing what programs, packages, or methods you’ve used, what worked and what didn’t.
Prepare for the technical exam
For a technical interview, it’s a good idea to review your ability to write complex SQL queries and prepare for simple coding exercises that focus on data processing and involve writing a script, explains Rudy Zinn, Director. Products at Fast AF Inc. These exercises are intended to reflect the technical skills needed for a day in the life of a data scientist.
Exercises may focus on how to work with aggregate datasets and window functions such as classifications, complex joins, and subqueries. Consider studying and reviewing sample SQL exercises; This can be especially effective in setting yourself up if you don’t use SQL regularly.
While SQL remains a widely used query language in business, it is not necessarily the primary tool for data science teams. Paul Bilodeau, CEO and co-founder of Filtered.ai, recommends finding people on LinkedIn who work for the company you’re interviewing for. Then take a look at what they listed as the projects they’re working on and the tools they’re currently using.
You can also explore what they post on Twitter and GitHub, which can help you figure out what topic you want to review.
Listing the types of problems a recruiter can work on is especially interesting. For example, if you are interviewing for a job that focuses on data analysis, you should know the difference between statistical distributions, know what they are used for, and be able to put them into practice. If your interview is for a machine learning-oriented role, be prepared to explain the models you’ve used and discuss potential deployment challenges.
Shawn Down concludes, “Most of the technical questions they have probably relate to the topics they are actively working on.”
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