From a Hiring Manager’s Perspective: What I Was Looking for in a Junior Data Scientist Role During the Interview

Originally published here

1. Understanding of data science concepts: Do you have a good grasp of the basic concepts, such as hypothesis testing and machine learning?

Without saying, it is important for a data scientist to have a strong understanding of data science concepts. Data science concepts will be tested in a job interview to see if the candidate has the required knowledge. Some common questions that may be asked include: How would you explain hypothesis testing to someone with no prior knowledge? What are some of the most popular machine learning algorithms, and why do you think that is? By being able to answer these types of questions, it will show that the candidate has a strong foundation in data science concepts and will be able to hit the ground running in their new role.

However, as I alluded in the intro, at least for my team, the weight I put on this question wasn’t as high as others. Let me explain.

A Hiring Manager Who Went Through 120 Resumes for a Junior Data Scientist Role

2. Communication skills: Can you explain your thought process and results effectively?

One of the most important communication skills during a job interview is effectively explaining your thought process and results. This is especially true for junior data scientist roles, where your communication skills will be tested to prove that you can work with frontline workers and team members. To effectively communicate your thought process and results, you need to be able to break down complex concepts into simple terms. Practice providing an example where you had to explain a complex concept or result to a non-data-savvy audience. The key word here is “practice.”

Practice providing an example where you had to explain a complex concept or result to a non-data-savvy audience. For example, if you were working on a project that involved predicting customer churn, you might have to explain the concept of churn to someone who doesn’t work in data science. To do this, you need to start by explaining what churn is and what it means for a business. You would also need to explain how your team could predict customer churn and the results of your analysis.

3. Motivation: Are you excited about working on data science projects in my organization?

When asked why I want to work for a particular organization during a job interview, my honest answer is always that I am genuinely excited about the opportunity to work on data science projects that can have a real impact. In my previous role, I was part of a team that developed a data-driven approach to improving customer satisfaction scores. For example, you can say, “I was able to see firsthand how our work made a difference in the lives of our customers, and I am eager to continue making that kind of positive impact in my next role. Given the cutting-edge nature of your data science initiatives, I believe this organization is where I can continue to grow as a data scientist and make the most impactful contributions to exciting projects.”

4. Independence: Are you able to work independently with little supervision?

Being able to work independently is an important skill for any data scientist, regardless of their level of experience. During a job interview, a potential employer may ask if you are able to work independently with little supervision. This question is designed to gauge your ability to take the initiative and complete tasks without a great deal of direction. The best way to answer this question is to provide a specific example of a time when you were able to work independently successfully. For instance, you might mention a project you completed entirely on your own or a task you took on outside of your normal job duties. By giving a concrete example, you can show that you have the independent problem-solving skills the employer is looking for.

An important part of working independently is being able to manage your own time effectively. Suppose you can demonstrate that you are capable of completing tasks without constant oversight. In that case, it will show the employer that you have the self-motivation and discipline necessary to be successful in a data science role.

3 Critical Aspects of Effective Stakeholder Management For Data Science Projects

5. Attitude: do you have the right attitude?

It’s no secret that attitude is important in the workplace. Your attitude can be the difference between a successful career and a series of dead-end jobs. So, it’s not surprising that employers place a great deal of emphasis on attitude during job interviews. If you’re interviewing for a junior data scientist role, here are a few tips to ensure you have the right attitude.

First, be humble. It’s important to be confident in your abilities, but you don’t want to come across as arrogant. Remember, you’re applying for a junior role. The interviewer wants to see that you’re willing to learn and humble enough to take direction from others.

Second, be positive. A positive attitude is infectious and can go a long way in making a good impression during an interview. Be sure to smile and exude confidence, even if you’re feeling nervous on the inside.

Finally, be prepared to answer questions about your attitude. Many employers will ask specifically about your attitude and how it relates to the job you’re applying for. Be ready with examples of times when your positive attitude made a difference in your work.

While technical skills are important, soft skills are just as critical for data scientists. In order to be successful in the data science marketplace, you have to stand out from the rest by demonstrating that you can communicate effectively with other team members, work independently, and be motivated to learn new things. If you can demonstrate these qualities during your interview, you’ll have a better chance of being offered the job. Lastly, to ace your next interview, remember to practice your answers because good communication skills are always a must for any role.