How to Stand Out in The Data Science Job Market
How to have the edge in your data science application
Applying for jobs right now can feel a bit difficult, sending countless applications with little to no responses. The problem is that you are probably not differentiating yourself significantly from other candidates, so in this article, I want to go over several ways you can make yourself stand out.
What is standing out?
Standing out is actually a relatively simple concept to get your head around.
All you need to do is be different to other applicants. You must be an outlier.
Sounds simple, right?
Like everything in life, it’s simple to understand but much harder to do.
The primary way to be an outlier is to do things other people are not doing. By definition, you will become an outlier.
Notice I am not saying you need to be better, although this is sometimes a by-product of being an outlier.
You just need to be different, and it only really needs to be in one dimension.
Let’s now go over some ways you can stand out.
Online Presence
There is a saying from Brazilian poet Mario Quintana:
Don’t waste your time chasing butterflies. Mend your garden, and the butterflies will come.
It’s essentially saying it’s better to create something to attract what you want rather than expend energy chasing it. There is a trade-off, but if I think back to my career, my biggest leverage has always been my online presence.
Apart from my first job, I never actually went looking for a role; recruiters always reached out to me via LinkedIn.
A good, polished resume is still great, but how are your other profiles looking? Is your LinkedIn and personal website active and optimised? How often are you posting?
The term “personal brand” is often thrown around, but it is so important. I know people find it a bit cringy, but the leverage it creates is insane.
In every mentoring session, I always tell the person to start an online presence; honestly, not many of them do. So, if you are that person who does it, by definition, you will stand out.
- At a minimum, I recommend having a nice-looking LinkedIn aligned with your resume / CV. They should complement each other nicely. See my LinkedIn for inspiration and my CV / Resume template here.
- The next stage is to start posting stuff. It can literally be anything, but it should ideally be related to your work. I would also add a personal website/portfolio to really bolster your online presence. Here is my website if you want some inspiration.
- The final stage is having something like a blog, YouTube channel, or newsletter that you post consistently. This is how you grow an “audience” and build an online presence of people who trust you and really show off your work.
As I said, applying for jobs directly is one way, but recruiters spend a lot of time looking for candidates as well, so having a strong online presence increases your chances of being approached.
I know many people reading this probably won’t do this, and that’s fine. But that’s precisely why building an online presence will make you stand out!
Unfair Advantages
Everyone has some unfair advantages they can use to increase their chances in the job market.
These are some of the most common ones you can probably use:
- Networking — It is well known, yet I still think it’s criminally underrated. Use that connection if you know anyone working in the field through family, friends, or even from past jobs. Obviously, don’t use the person, but the worst they can say is no, and you move on. Also, often, it is in their best interest due to the referral bonus.
- Educational / Job Background — If you have a background in marketing, finance, medicine or anything that’s not directly related to tech, that’s your advantage. You already have the domain knowledge in a specific area, which will complement your data skills. So, apply for jobs in that business industry, and you will stand out as you have domain knowledge.
- Where You Live — Hear me out; everyone wants to work in a big city for a big company, and it is great to have those aspirations. However, if you are starting out, there is nothing wrong with going for a smaller company nearby. There will be less competition, and you will still learn a lot. I did this for my first job, where I had to spend half my time in London and half in Sussex. My parents live in Sussex, which made it much easier for me!
There are so many more things I can think of, like speaking a different language, playing a particular sport, being really good at one bit of software, etc. The list is truly endless.
You have to find the things you are better at than most people and use that in the job market. It can be tricky, but if done well, it can really benefit you.
Build Something Cool
I regretted having too many “easy” projects when applying for my first data science job. It was good that I had projects, but they were mostly the same, and I just changed out an algorithm.
In reality, what works well is having one significant quality item that can “wow” the interviewers and be a great talking point during the interview — it could even take up the whole interview!
My primary approach would be to develop a project you don’t think you can do, but do it anyway. You will soon learn that everything is figure-outable.
To be more concrete, I am saying don’t just build a model in a Jupyter Notebook; make it way more interactive. You should
- Deploy the model on a cloud provider like AWS.
- Add some unit tests and make them align with software engineering best practices.
- Figure out a way for it to make live predictions.
- Build a monitoring dashboard that can accessed online.
- Store historical predictions in a database.
I am essentially describing deploying and monitoring your model end-to-end, which is still only one approach. However, you can see how this type of project is of significant difficulty and quality compared to a static model in a notebook.
So, think of a project that would take a couple of months to complete and is something you are likely to struggle with.
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How to Stand Out in The Data Science Job Market was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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