WHY CAN’T YOU FIND A JOB?
As a Data Science mentor, I’ve always dealt with beginners having trouble in finding their first job. Even though we have a booming job market with so many positions for data professionals, these people keep complaining that they can’t even get to an interview. For those more experienced, this is very atypical, we get several messages, I would say on a weekly basis at least. So, what’s wrong?
I would summarize the problem into two perspectives:
- The job market doesn’t see you as a proper candidate: You are having trouble in sending the message "I AM A DATA PROFESSIONAL" to the market. People who didn’t graduate in STEM are the major public in here, but not only them (I'll soon tell you why I);
- Recruiters can’t find you: This is such a simple problem to fix, that I keep repeating to my mentee over and over again. Most recruiting tools are automatized nowadays, so it’s usually not a person that is actually looking for you, but a robot (a program, to be more precise). You need to be found by it!
RIGHT, WHAT SHOULD I DO NOW?
Now, take a look at the 3 tips below, I guarantee you that recruiters will start calling you about their positions:
TIP 1: HAVE A PORTFOLIO
Last year, I recruited two entry-level data scientists and I have to tell you that the market is booming on the demand side, but also on the supply. We received more than 50 resumes in less than a month, being a startup. Can you imagine what’s happening on the big companies? Can you imagine how many resumes they receive on a daily basis?
There’s too much competition, you need to stand out among other candidates. Besides, we have no idea how much you know regarding data science, machine learning, statistics, Python and so on. Having a portfolio is the perfect strategy to stand out between so many resumes. It’s like you are right in front of us, programming and saying "Hey, this is everything I know, look at this dataviz, look at this model etc etc".
Please, start your portfolio right now! It’s a must! If you have no idea on what to do, here a few links which might help you:
- https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9
- https://medium.com/@bhavyrajdev0/my-first-full-stack-data-science-project-part-5-5dd808676423
- https://www.youtube.com/watch?v=PwE-xuw5sNU
TIP 2: USE SOME KEYWORDS
As I mentioned in the intro, the search for job candidates is made by machines and you need to appear in their search. The simplest way of doing that is by using the correct keywords. If you have a strong knowledge in Python and in SQL, mention both tools in your Linkedin (and in your resume).
Take a look at your Linkedin right now, check if you mentioned all the programming languages you know, all the dataviz tools, the statistics techniques, the Machine Learning models and so on. Basically, I’d recommend you to have a headline containing two or three data terms you’re willing to work with and one or two languages (or tools) you have some proficiency. If you are only a beginner and feel insecure about this type of headline, you may add some word to express this level of knowledge (e.g.: "Data science enthusiast"). These are my choices for the headline and the "About" section:
TIP 3: THE LESS GENERIC, THE BETTER
Now, we have already shown the job market we know something regarding data science and the machines can find us in their search. However, we still need to convince the recruiter (and other stakeholders) we are problem solvers. What do I mean by that? Take a look at these two descriptions I found on Linkedin:
When people think about data scientists, they think about a crazy genius capable of understanding complex mathematical models, or maybe about great programmers. In spite of that, we are business professionals. Our job is to find solutions using data. You can build a CatBoost with 90% of accuracy using hundreds of features and several feature engineering techniques, but you won’t impress anyone if that model doesn’t solve any problem. If your model doesn’t increase revenue, or reduce loss, or anything like that, it’s worthless. Both descriptions I showed you are trying to explain the solution and present the results (quantified) to the reader, not talk about algorithms.
That’s why you should mention numbers when talking about your past experience. Show recruiters you aren’t only good in math and in programming, tell them about the problems you solve in your career. Did your model increase the revenue? Did it reduce the company loss? Did your program reduce the number of hours of the previous program? Can you quantify this?
Ok, you might be saying "dude, I’ve just left college, I’ve never done anything like this". Well, tell them about your volunteer experiences, other internship experience, being the president of any group, all these sorts of things. Just try to show them that you know you must focus your skills on problem solving, not only building machine learning models.
LET’S DO THIS!
I know some might be thinking that only 3 tips won’t change much, but I guarantee you that by doing all of these you’ll at least double your chances of getting a data related position. Keep improving your profile, keep becoming an attractive candidate for the position, don’t stop studying, you’ll soon find what you want!
