FAQ - How can you stand out as a candidate?
This post is inspired by two FAQ I see pretty often:
How can I stand out as a job candidate?
Do I need to learn X to be a data analyst/data scientist?
How are they related?
Well, one way to stand out is by not doing the bare minimum.
I get it. You want to land your first job as quickly as possible, so you don’t want to waste time learning skills that aren’t necessary.
And there are multiple influencers out there saying you just need to learn some SQL, which you can do in a few weeks, and that’s enough to land a job. Math isn’t necessary. So why waste your time learning stats?
I say this a lot but I’ll say it again. Get career advice from multiple people.
One thing that is true about this field - and likely many fields - is there are multiple career paths. Another truth is that things change over time.
This means - 1) there are multiple paths you could take to become a Data Analyst or a Data Scientist, but also, 2) what it took to land a job in the past might not be what it takes to land a job in the future.
Everyone sharing advice means well, but their advice might be outdated. Or they might not realize that they are an outlier, and not the norm, which means it’ll be harder for others to replicate their success.
As a Data Analyst or Data Scientist, your job is to look for patterns. To look at what is normal and repeatable. So if you’re trying to figure out what it takes to land a job, don’t just talk to one person. Talk to many people and look for patterns. Trying to follow an outlier career path means at best, you’re going to be wasting a lot of time, and at worst, you’ll never be able to replicate their success. Or their advice is outdated and you’re wasting your time.
But another thing to keep in mind - if you are trying to stand out, that means you can’t just do what everyone else is doing. If the bar is low enough that you can learn some SQL and Tableau in just a few weeks - well so can everyone else. You won’t stand out if all you have is those skills listed on a resume. Yes, even if you did a certificate. Not enough.
Also, if you are planning for a career and not just a job, you have to think long-term. Data is a changing and evolving field. There are always new tools coming around, new use cases, new technologies, new ways to solve problems. And your boss or future boss will want someone who can evaluate all that new stuff, learn what’s relevant, and apply it on the job by solving real problems. What I do in my job today is more advanced than what I did when I was hired into this role 4 years ago. Granted, I picked up a lot of skills thanks to my master’s degree. But the types of projects my boss wants us to do these days are more advanced than what the person who originally hired me wants to do. Meaning the type of skills I’m using today are more advanced than what got me my current job.
Having a broad skillset is an asset for a career in analytics/data science. It’s a broad field. If you want to be a valuable employee, you need to be able to solve a variety of problems.
If you just want to learn a few skills and then be set in a career for life - this might not be a good field. I don’t know what would be a good field instead. But this is definitely a field where you have to be a lifelong learner, otherwise, you’ll eventually be left behind.
Anyway, so how do you stand out as a candidate?
I know this is trite, but I’ll summarize what someone on HR TikTok shared - by having the skills listed on the job description and relevant experience at a similar company.
When a hiring manager is evaluating candidates, they are considering risk. Hiring someone is a risk. They want to minimize that risk as much as possible. I see tons of aspiring Data Analysts asking for a hiring manager to “just take a chance” and hire them. But the reality is, a hiring manager doesn’t want to feel like they are taking a chance, they want the candidate who is most likely to be the sure thing. The one who has the most relevant skills, who has applied them in a similar role solving similar problems.
So what can you do?
Read job descriptions. Look for patterns. What skills do hiring managers want for the jobs you’re targeting?
Network. Talk to people in the job you want. What skills do they use the most on the job?
Combine the lists from the above two points (hopefully they’re really similar) and focus on getting really really good at those.
Demonstrate those skills.
If you’re working, no matter your title, and you can apply those skills - awesome. That will be the best experience. Make sure that work is on your resume.
If you aren’t able to do that - then look for other opportunities to apply those skills. This post has a list of ideas for getting hands-on experience if you can’t get it on the job.
Also - develop “soft” skills. This is something a lot of entry-level candidates lack, so if you have these skills, you can actually stand out. (Assuming you have the necessary tech skills and some projects to demonstrate your skills.) I’ve also heard these referred to as “skills that can’t be automated,” so if you’re worried about AI taking your job … work on developing these skills.
“Soft” skills are things like:
Being able to communicate clearly.
Being able to identify problems you can solve with data.
Demonstrating the initiative to try to solve them.
Connecting your data analysis to business value or impact.
This post has suggestions for developing and practicing soft skills.
Also, don’t be afraid to get other experience if you have none. Just because you don’t start your career in a data role doesn’t mean you can’t land one later on. Lots of folks working in this field started their career doing something else, were able to get their hands on data while doing that “something else,” and used that experience to pivot into a data-focused role. I started in marketing, but I know others who started in customer service, client support, account management, IT, finance, accounting, product management, research, and project management.
Got a question? Submit it here, and I might answer it in an upcoming newsletter (or on my blog or on TikTok - or all three).
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