Learning the technical skills at a data science bootcamp is only the first step in a journey to working in tech. Landing your first role can be intimidating and imposter syndrome can affect even the most seasoned professionals. Jen Raimone, Director of Career Services at Metis breaks down the top two soft skills a data scientist needs to land the job and launch a successful career. Find out Jen’s 4 tips for managing stress in the interview, ways to demonstrate your soft skills both in the interview and in your job materials, and how Metis prepares students to interview like pros.
First, banish the idea that an interview is only about technical skills. Businesses look at your baseline and potential for work, but they also look at who you are as a person. “Technical” questions can include asking you about a time you used data to solve a problem or about a time when you weren’t on the same page as a colleague and the solution to that problem.
It’s important to use the job description as your first guideline on what the employer cares about and potential questions that may be asked. If certain concepts are continuously brought up, you know they’re important to the company or team. If you know you’ll be working with clients, you can expect to be asked client-facing questions; you will want to be prepared to provide examples of how you worked with them, when you went above and beyond, or how you managed an important or difficult client. Think about these case scenarios and then even if they don’t ask about them, you’ll be prepared.
Listen to what the interviewer says in an interview because that’s also your cheat sheet. When I’m coaching graduates or teaching my team, I say to take the offensive in an interview. What you know about the company from talking to people will be your outline for what’s important. One mistake job-seekers make is getting anchored to a specific question in the way it’s asked. Instead, take a step back and look at the core of the question. Figure out the theme of the question you were asked, and then base your answers on that instead of getting hung up on specific wording. If you prepare for a specific question and it’s asked in a different way, it can throw you off.
Data scientists today are expected to have a strong emphasis on business acumen. Since the pandemic lockdown, more companies are open to having data science professionals work remotely and this has highlighted the need for these soft skills.
#1 Soft Skill: Communication
Communication is at the top of the list of non-technical skills for data scientists. Whether a data scientist is working as a consultant for clients or at a company within any industry, they need to be able to explain where they got their assumptions or conclusions. In addition to justifying their conclusions, data scientists need to be able to explain those conclusions in ways that everyone can understand, not just industry professionals.
For example: if you’re giving a presentation to your colleagues on recommendations for reengaging customers who are at risk of canceling their membership, you’ll lose the room if you start talking about how this confusion matrix reflects the performance of a binary classifier and explaining true positives and false positives. Your job as a data scientist is to be able to translate your findings into something accessible.
#2 Soft Skill: Active Listening
Another soft skill related to communication is active listening. While communication is a two-way street, it’s important for data professionals to be able to pay attention and comprehend instead of simply providing a response. Businesses want data professionals that are able to accurately translate between technical and non-technical concepts.
Active listening is also really important when interviewing for a job. When I do mock interviews with Metis students, I often see students answer a question that I didn’t ask. For example: I’ll ask why they’re interested in the company and they’ll tell me about their background. This is partly because they’re nervous and partly because they’re making assumptions based on previous interviews.
I tell my students that not actively listening to an interviewer may signal to an employer that you can’t follow directions. An employer might wonder if you're unmanageable because you want to do things your own way. All of this might not be true, but in an interview you have a limited window of time to make an impression. You might think you did well in an interview, but if you weren’t actively listening, then the employer may have experienced something different.
Jen’s Top 4 Tips for Managing Stress in an Interview
Resumes are written in third person and they're generally to the point. Good written skills often translate to good verbal communication skills. Blog writing and cover letters are a great way to highlight your skills and stand out. I see a lot of resumes and LinkedIn profiles, but there aren’t many places where you can be too distinct. Linking your personal blog or even just posting on LinkedIn can help translate those skills.
How do in-person interviews compare to online interviews?
It can be difficult to engage in a virtual interview. The correct placement of your laptop and looking into the camera are especially important, even if that sounds trivial. The nice thing about interviewing virtually is that you can have notes to yourself on your screen that you can refer to during the interview. You don’t want to be reading off of these notes directly, but you can reference them and your resume much easier than in an in-person interview. This can take a lot of the pressure and stress away from the interview process.
At Metis, we are looking for incoming bootcamp students to have good communication skills. We’re looking at things like how a prospective student explains concepts, if they’re verbose or difficult to understand, how they interact via email, what kind of effort they put into an application, why they want to learn data science, and why they want to work in the field. It’s important to us that a student is getting into data science because they have genuine passion for it. We also assess our incoming students on soft skills, like resourcefulness, passion, initiative, and how they respond in stressful environments.
Developing Soft Skills at Metis
Since communication is at the core of the soft skills that data scientists need to have, we make sure to help students develop better communication skills while at Metis. For example, Metis students have the opportunity to practice these skills through in-class presentations where instructors give them immediate feedback. Being able to concisely discuss your technical portfolio in a job interview is a way to demonstrate your soft skills.
We spend time doing mock interviews with our students. When I do mock interviews, I have students write down their intention and the message they would like to get across in the interview. This message should be what makes a student stand out. After they write it out, I have them put it away. At the end of the interview, we discuss their intentions and I tell them if they got their message across. It’s a great way to show a student how to calibrate their performance in an interview.
My careers team also helps students with self-promotion. If a student has a cool project and they’re underselling it, we’ll let them know. We help our students understand what an employer might see. Underselling your own project may look like you’re lacking depth which isn’t true.
The Importance of Feedback at Metis
Sometimes students have a hard time taking feedback and that can translate into other issues during their future job interviews. Some examples of being resistant to feedback include:
When we see a student is having trouble with feedback, we address this with them. Many times, people are just not aware of how they are coming across and may be resistant because they’re perfectionists. These students may be coming from a good place, but it’s our job at Metis to help these students hone their communication and active listening skills so they won’t miss out on a great job opportunity.
What types of jobs are Metis grads landing?
Metis students land data roles in a variety of industries, and find work at companies like Meta, Accenture, Spotify, Google, CKM Analytix, Capital One, GrubHub, Booz Allen Hamilton, Capgemni, and Deloitte. We also have students who enroll at Metis in order to upskill. Those students come from consulting or management positions who use their new data science toolkit on the job. The roles Metis graduates work in are anywhere from Data Analyst to Data Scientist to Machine Learning Engineer. The level of role depends on the experience a student has going into the bootcamp. That said, we see folks from non-technical backgrounds that are now working as data scientists. They might be entry-level jobs, but they’re data scientists now!
Find out more and read Metis reviews on Course Report. This article was produced by the Course Report team in partnership with Metis.
Jess Feldman is the Content Manager at Course Report. As a lifelong learner, Jess is passionate about education — She loves learning and sharing insights about tech bootcamps and career changes with the Course Report community. Jess received a M.F.A. in Writing from the University of New Hampshire and lives in southern Maine.
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