Get Hired as a Data Scientist with Five Quick Tips

Tip #1 — Create and maintain an up to date GitHub profile and/or a blog where you show off your personal data science projects#

This obvious tip can help you get your first data analyst or data scientist job.

If you’re looking for a data science or data analyst position, try to think about it from the perspective of the technical recruiter. You might look at the person doing the hiring as your opposition, someone whose job is to ask hard data science questions and scrutinize your resume for every possible imperfection. However, that is usually not the case. Think of them as a potential business partner for a certain period of time. Most of the time they are as motivated as you are to hire the best candidate to join the company.

There are two reasons why it is a difficult task for them:

• Sifting through hundreds or thousands of applications and evaluating each one is not easy.
• Interviews do not showcase the whole person. Recruiters do not see all of the strengths and all of the weaknesses of a candidate, and there is often an element of luck.

Therefore, you need to be a candidate who makes the hiring process and decision easy.

A job interview is an opportunity to show what you can do and to show that you understand the needs of the company you are talking to. Demonstrate that you are already a member of the team; you just have not been hired yet.

This is where personal data science projects come in handy and help you stand out as a candidate. I am not talking about projects that you do as part of a bachelor’s degree or a data science course, because everyone who finished the course has already done those projects. Standard projects are useful, but to land a data science job they are nowhere near as useful as the projects that you do yourself on subjects that interest you.

These projects are more interesting to recruiters because they show your ability to apply the knowledge that you have. They give interviewers an insight into how you work, how you think, and how competent you are. They also give interviewers something to talk about and questions to ask you. Your job is to make the hiring decision easier.

A single personal data science project can show your ability to frame a question and answer it properly. Try to answer a question that does not have a tidy data set so it also shows your ability to gather data from different sources, combine it, manipulate it, and clean it. Show how you can use visualization methods to communicate your data and findings.

These projects are also useful because if you struggle with part of a technical interview but have already demonstrated proficiency in the same topic in one of your projects and can talk about it clearly, your interviewers are more likely to be forgiving. You still need the subject knowledge to answer technical questions, but projects are what set you apart.

Tip #2 — Don’t overstuff your resume with irrelevant or vague skills and achievements#

For a data science role it is unnecessary to mention things like MS Office, Agile methodologies, or source code management without context. Come up with an itemized list of relevant skills such as programming languages, machine learning libraries, visualization libraries, and project management tools.

Overstuffing a resume with every possible data science buzzword is also a bad idea. It may get past a machine screen, but it will put off a human recruiter.

Tip #3 — Use precise and concise descriptions in your resume#

One example is to clearly mention the country or countries of your past and current workplaces. This can be important when companies have constraints on visa sponsorships. If you omit this information, you may waste everyone’s time by starting an interview process only to discover that it is a blocker.

Tip #4 — Don’t make the resume long#

There are no fixed rules here, but from my experience, one page is more than enough for anyone with less than five years of relevant data science experience. More senior candidates with more than five or ten years of relevant experience can use two pages.

Of course there are exceptions. For example, PhD and postdoctoral candidates may want to list publications. Use your best judgment and mention only the publications that are most relevant to the role. A five-page resume with every publication listed may put a recruiter off.

Tip #5 — Show interest in the company and the position#

Do your research about the startup or the mature tech company you are applying to. Show interest in the job itself and have questions for the recruiter or prospective teammates such as:

• How many people are in the team?
• What organizational approach is used for data scientists: centralized, embedded, or integrated teams?
• What is the typical split of work?
• What is the full interview process like?

This kind of question shows the candidate’s own interest in the advertised position. Not asking these questions can be a red flag for some recruiters or prospective teammates.