Standing Out to Data Science Recruiters & Hiring Managers

So you want to get a data science job and make your profile stand out to potential hiring managers and recruiters -- what do you do? Well, you're in luck because that's what I'm here to help you with today.

Keep in mind this is a partial list, but what I've seen work based on my experience as a hiring manager, mentor, and educator.

Hooking the recruiter

Understanding the role of the contact you're speaking with is critical knowledge throughout the interview process because each phase differs. For example, if you're in an initial interview with a recruiter or HR contact for the role you've applied for, understand that the recruiter may not have a technical background. It's best to assume they have limited domain knowledge. Instead, their role is to screen candidates and ensure the qualifications you've listed on your resume match the requirements of the job requisition, plus gather relevant information such as salary expectations, visa sponsorship status, etc. Often, the hiring manager will direct the recruiter to look for preferred candidate characteristics beyond the minimum and preferred qualifications listed on the job posting; Therefore, ask your recruiter what specific traits they're seeking. If they have an answer, that is the opportunity to step up and speak to your experience and qualifications for that precise fit.

Meeting the Hiring Manager

Great news! You've passed through the initial screen, and now you're scheduled to speak with the hiring manager -- treat it as an opportunity. This person is your potential future boss, so it's time to focus and make a strong impression so you'll stand out from the competition. This conversation covers a lot of ground: in addition to assessing your technical skills and relevant educational and industry experience, the manager will look at how you solve problems. I cannot emphasize this point enough: managers are gauging your creativity and critical thinking skills and will want to hear how you've worked on problems and projects in the past. The manager may also present you with a mock scenario, which is often a project the team is currently supporting.

When discussing previous projects or going through a mock project scenario, focus on understanding the problem and how the problem statement or research question relates to the entire project lifecycle. Of course, the exact project management style will vary by company. Still, the manager will expect you to understand and describe the steps you'd take from gathering requirements and formulating the business question, data acquisition, exploratory analysis, feature engineering, model building, validation, and deployment or delivery.

Another tip to help you stand out: hiring managers prefer to see unique, individual work versus the same Titanic dataset everyone has worked on and committed to their GitHub repos. So, if you're a grad student with a thesis that involves a novel research question, dataset, or methodology, now is your time to shine! Be proud of your work, and talk about it in detail! If you don't have a thesis, you can always take a dataset you're familiar with, such as measurements from a smart thermostat, analyzing your social media feed, or ingesting a publicly available API, and create a project from that. Remember: data science is a science: meaning you're making hypotheses and testing your hypothesis based upon observations. Demonstrating that you can ask appropriate research questions, define a problem statement (industry-speak for hypothesis) and test your assumptions using the proper methodology is the sine qua non of the data scientist's job. To give you an anecdote from personal experience, I love college football and built models predicting several target outputs, such as game scores, win probabilities, returning performance, etc. I was passionate about the subject, but more importantly, I was curious to learn about the hidden patterns and test my assumptions about the sport. Demonstrating that enthusiasm and ingenuity landed me my first two data science jobs precisely because I had a unique portfolio and story.

Finally, lean into your technical competencies. With most data science projects, there are multiple paths by which you can complete each phase of the project. For example, if SQL is your strong suit, present ways to generate value for the role through your core competencies where appropriate, such as feature engineering or exploratory analysis. On the other hand, you might be strong on the DevOps side, so you could speak to your capabilities in developing coding and repository artifact standards, project templates, design patterns, and more. Feel free to emphasize your strengths so long as it aligns with the role, as most hiring managers will accept a certain degree of on-the-job training to boost your skillsets elsewhere if you're demonstrating value in other ways.

Conclusion

Securing a data science job requires a strategic approach to showcase your unique strengths and experiences. Start by understanding your audience, whether a recruiter or a hiring manager and tailor your communication accordingly. Emphasize your problem-solving skills, creativity, and critical thinking throughout the interview. Showcase your work using unique projects or datasets to stand out. And finally, lean into your technical competencies and demonstrate how they add value to the role. By following these tips, you'll be well on your way to making a strong impression and landing the data science job you've been aiming for.

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