A typical data science job posting can draw dozens or even hundreds of applications. For that reason, the most important hurdle to overcome during the application process is usually to find a way to capture a company’s attention.
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So a job application isn’t just a contest of skill: it’s also in large part a contest for bandwidth. And the earlier in the application process, the more applications you’ll be competing with, and the more challenging it will be to get eyeballs on yours.
But how do you ensure that your application will get a second look-over when decision time comes? Two options might come to mind:
- Optimize the crap out of your resume.
- Try to skip the resume screening step altogether.
To be honest, I don’t think there’s much value in discussing point 1 here. Some reasons:
- It’s a topic I’ve covered before, and there are plenty of great “here’s-how-you-can-build-a-perfect-resume” posts out there already.
- Unless your resume is a real dumpster fire, it’s impossible to optimize it very much because every recruiter prefers a different format, and different content.
- Sending your CV and not getting invited to an interview is a zero-feedback process, so you won’t be learning about what you can improve with each application if all you do is fire your resume into the void over and over again.
For these reasons, we put a lot of emphasis on strategy 2 at SharpestMinds. So in what follows, I’ll be focusing on exactly that: finding ways to sidestep the conventional resume screening process.
Hint: the answer is networking.
How to skip the line
A vastly disproportionate number of hires are the result of referrals from employees who already work at a company. So your best way in, nine times out of ten, will be through a relationship with someone who works at your target company, rather than a generic channel, like a jobs board (which inevitably leads to the dreaded resume screen).
Relationships are great because they give you a signal boost, but they also make it much more likely that you’ll get feedback on your application. Even if your application gets turned down, you can always turn to your point of contact and ask how you can improve.
But how do you build meaningful relationships with established data scientists? We’ve seen this done successfully in two ways:
1. Through Meetups
Hands-down, Meetups are the single best way to network your way to an entry-level data science position. But you have to choose the right ones.
The best Meetups have the following three characteristics:
- They’re technical, and not for beginners (good companies don’t show up to “intro to data science” Meetups to hire)
- They repeat often, at least once a month (ideally weekly or more often than that). This allows you to get to know who’s who in your local community, and lets them get to know and recognize you.
- They’re not too big (>100 people regularly attend) or too small (<15 people regularly attend).
Once you’ve found a good local Meetup:
- Start attending. You don’t have to talk to anyone the first time you go, but fairly shortly you’ll want to make your presence known by asking questions after the talks, and making an effort to chat with other attendees. Over time, this kind of activity compounds, and makes you a familiar face to all the Meetup’s attendees.
- Set yourself concrete objectives for each Meetup. Aim to have at least 3 substantive conversations with other attendees each time you go, or force yourself to ask at least one question after the talk.
- One great, high-leverage activity is to ask the Meetup organizers to give a talk. This helps to raise your profile in the community, and also forces you to up your technical game ahead of your presentation.
- You can add anyone you speak to at a Meetup on LinkedIn. If you add someone to your network in this way and they’re a particularly promising lead (i.e. you have similar technical or industry interests, or are unusually well-connected in the data science community), you ask them to grab a cup of coffee and talk about their job and experience. From there, follow the later steps in the LinkedIn playbook below.
2. Through LinkedIn
Ok, this one’s pretty systematic and tedious. But that’s exactly why other people don’t do it, and why it’s a great way to stand out and give yourself a signal boost:
- Go to LinkedIn
- Search you area for “data scientist”, “machine learning engineer”, “data analyst”, or whatever search term is most appropriate.
- Find at last 20 profiles of established data scientists (with >6 months of experience), who seem to have similar technical interests to you and who work at companies that are currently hiring (you can check their company websites to see if that’s the case). Ideally, you should have more in common with them than just the fact that you’re both into data science: NLP, big data/data engineering, computer vision, finance, etc are all great sources of extra common ground.
- Send each a connection request, with an appropriately customized version of the following message:
Hi [their name],
I just saw your profile and found your work on [data analytics for retail / data visualization in real estate / etc] very compelling (it’s an area I’m particularly interested in). I’d love to connect!
Thanks,
[your name]
- Most of them won’t connect. Some of them will. Track your reach-outs (I recommend Trello). Follow up with the ones that accept your connection request:
Hi [their name],
Thanks for connecting! I’m really interested in learning more about the work you’re doing at [company name]. I’m hoping to work in a similar area, and would love to pick your brain about the challenges of working in the space at some point.
[your name]
- If they respond, and they seem open to it, follow up with a third message, asking if they would be willing to meet for coffee [somewhere close to where they work!].
- You may get multiple bites. If you do, great: build your relationship with each person separately. Your goal in each case should be to learn as much as you can about the technical problems that they face in their day-to-day work, so that you can determine whether you’d be in a position to provide added value to their company as an employee. If you don’t think that’s the case, focus on building your skills up before trying to leverage your new connection into an interview.
- If you feel based on your conversations with your new connection(s) that you’d be able to hold your own in the roles they discuss with you, that’s a good sign that you should directly ask them— without pressuring them — whether you’d be a good fit. Listen carefully to their answer: if it’s not an invitation to apply, you can ask (without being pushy) why you’re not a good fit yet.
- A good way to do that: “Thanks for clarifying that for me, it’s very helpful to know where I stand as I continue to build my skills. Where do you think I would most need to improve in order to position myself better for a similar role in the future?” The goal here is to come off as knowledge-hungry and not job-hungry.
- If things go well, ask your new contact if they would be open to meeting with you again once you’ve made a bit more progress, and ask them for their permission to keep them updated as you reach some key skills development milestones (but only if you intend to do so: there’s nothing worse than committing to doing something and not following through).
A general word of caution as you apply these strategies: be genuine, and don’t treat your new contacts as stepping stones. They’ve generously volunteered themselves as nodes in your network, and you should treat them with respect. The data science community — and the tech community more broadly — has developed a strong “pay it forward” ethos, precisely to help aspiring developers overcome the high technical barriers to entry they face when they try to enter the field.
That’s a great thing, and we owe it to each other to keep the good vibes going.
So the best mindset to apply to these situations is to treat your contacts’ time as an investment in you, and to do everything you can to reward that investment and pay it forward the day you get a cold reach-out on LinkedIn by someone whose shoes you once filled.
