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 will 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 of it from the perspective of the technical recruiter. You might look at the person who is doing the hiring as your opposition who’s job is to ask you really hard data science questions and scrutinize your resume to find any possible little imperfections such as lack of experience, not mentioning the data tool or programming language they utilize in their data stack etc. However, that is just not the case. Think of them as your potential business partner for a certain period of time. Most of the time they are as motivated as you to seal the deal and hire the best candidate to join their company. There are 2 reasons why it is a difficult task for them:
- Sifting through hundreds and thousands of applications and evaluating each of them is not easy
- Interviews don’t showcase the whole person and they don’t see all of the strengths and all of the weaknesses of candidates, and there’s 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 for you to shine, to show off what you can do and to show that you understand the needs of the company that you’re talking to. Demonstrate that you’re already a member of the team you just haven’t been hired yet.
Here is where personal data science projects come in handy and enable you to stand out as a candidate. I’m not talking about the projects that you do as part of your bachelors
or data science courses, because everyone who finished the course must have already done those projects. Yes, these standard projects are useful, but in order to land a data science job they are nowhere near as useful as the projects that you do yourself on subjects that interest you.
The reasons these projects are more interesting to potential recruiters it’s because they showcase 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 the interviewers something to talk to you about and questions to ask you. Remember? Your job is to make their hiring process and decisoin easy.
One single personal data science project can show your ability to frame a question that you then go on to answer and to frame it properly. Try to answer a question that doesn’t have a tidy data set so it 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 personal data science projects are also useful because if you mess up part of your technical interview, but have demonstrated proficiency in the same data topic on one of your projects and are able to talk about it your interviewers likely to be more forgiving. Yes, you will need the subject knowledge to be able to answer the technical questions but it’s projects that will set you apart.
Tip #2 — Don’t overstuff your resume with unrelevant and very vague skills and achievements
For a data science role it really unnecessary to mention something like MS Office, Agile Methodologies or Source Code Management. Come up with an itemized list with of relevant skills such as: programming languages, machine learning libraries, visualization libraries, project management etc.
Overstuffing the resume with each and every possible data science buzzword is also a bad idea. It may go past the machine screening, but will put off a human recruiter.
Tip #3 — Use precise and concise descriptions in your resume
One example is to necessarily mention the country/countries of your past and current workplaces. This is very important since some companies have constraints on visa sponsorships. If you omit this information, you might just waste everyone’s time starting an interviewing process and both realizing that it is a blocker.
Tip #4 — Don’t make the resume long
There are no defined rules here but from my experience for anyone with less than 5 years of relevant data science experience 1 page should be more than enough. More senior candidates with more than 5 or 10 years of relevant experience can take 2 pages. Of course there are exceptions, for example PhD or post PhD candidates who might want to list all of their publications. Use your best judgement, and mention only your top and the most relevant to the data science role publications. A 5 pages long resume with every of your academic publications listed may put a recruiter off.
Tip #5 — Show interest in the company and the position
Do your research about a young startup or a mature tech giant you are applying to. Show interest in the job itself and have questions to the reqcruiter or prospective team-mates such as:
- How many people are in the team?
- What organizational approach is used for data scientists: centralized (stand-alone), embedded or integrated teams?
- What is the typical split of work?
- What is the whole interview process like?
It will indicate the candidate’s own interest in the advertised position. Not asking these questions might be red flag for some tech recruiters recruiters or prospective team-mates.