are you sitting on right away?
10? 50?
Possibly you’ve crossed 100 and also you’re beginning to wonder when you’ll ever break in.
Well, I’ve been there myself.
I sent over 400 applications before securing my first data science job.
Nonetheless, over the past few years, I actually have landed $100k+ job offers from firms like Gousto, Deliveroo, DoorDash, Sensible, and a few startups.
So, in this text, I’m breaking down the precise mistakes I made so you’ll be able to skip the struggle and fast-track your path to a high-paying data science profession.
Let’s get into it!
Useless Learning
The very very first thing you might want to do to get a knowledge science job is clearly learn some data science.
The issue is that it’s really easy to learn completely useless information that will not be actually needed when attempting to land a job.
I spent weeks learning topics that I used to be never asked about or utilized in any interview process I actually have been in. And, I actually have passed through well over 100 interviews at this point.
Things like AWS, Docker, unit testing, etc., rarely come up in interviews. I mean, how can someone really test your AWS knowledge in 1 hour?
Yet many others and I spend time learning these topics though it’s an entire waste of time when you wish to get hired as quickly as possible.
I encourage all my coaching clients to keep on with studying the basics:
- Probability theory
- The essential supervised and unsupervised learning algorithms
- Easy to medium Leetcode questions
- The steps behind constructing a machine learning model
- Statistical testing and experimentation
- The elemental machine learning concepts like gradient descent, bias vs variance and cross validation
These are all areas that all the time come up in interviews and are where you need to invest your time.
You have to ruthlessly prioritise learning the basics, as they pay probably the most significant dividends in the long term.
Scatter Gun Approach
It shouldn’t take 400 applications to land your first job.
It took me that long because I used to be using the “scatter gun” strategy. I used to be spamming LinkedIn’s Easy Apply like there was no tomorrow.
The success rate and numbers speak for themselves: this method resulted in only a few interviews.
What I must have done is employ the “sniper” method and hone in on roles where I had a transparent advantage.
I reckon you’re pondering,
This is solely a myth.
Everyone has a bonus; you only haven’t found it yet.
For instance, you’ll be able to goal roles where you will have…
- A university thesis relevant to a selected industry.
- Side projects that solve an organization’s exact pain points.
- Living in a location with less local competition.
Also, don’t shoot for the moon right off the bat.
You’re not possible to get a FAANG offer if you will have no prior experience, unless you attended a wonderful school and were top of your class.
The vast majority of people should start at smaller firms and slowly work their way up. It’s precisely what I did, and it’s a rather more sustainable strategy.
Stop wasting energy on roles you aren’t a fit for and begin aiming where you’ll be able to actually win.
Un-optimised Resume
My first resume completely sucked, like, truthfully, it was complete dogwater. I’m even surprised that I landed a task ultimately.
The reality is, most individuals think their resume is nice. Nonetheless, I actually have reviewed tons of of information science resumes, and most of them are pretty bad.
I actually have an entire article explaining what an amazing data science resume looks like, but let me break down the important thing points here.
- Use a clean template with easy formatting. You will discover my one here.
- Keep it to at least one page, unless you will have a decade of relevant experience.
- At all times mention metrics, numbers and particularly financial impact.
- Lead together with your expertise, as that’s what recruiters mainly hire for.
- Use motion words like “led”, “developed,” “executed,” and “conducted.” You should be clear that you simply did these items.
- Don’t spam too many programming languages and technologies; it is a red flag, since I doubt you recognize all of them.
It’s truthfully not too hard to create an amazing resume; you might want to put within the time, and I’m talking at the least 10 hours.
It might sound like so much, however it’s probably the most crucial document in your skilled life, so why attempt to low cost out on it?
Tailor Your Resume
Every application I submitted used the very same resume.
The identical generic stuff, not personalised in any respect for the corporate or role I used to be going for.
On this market, being generic and basic doesn’t cut it.
What I must have done and what I tell every coaching client I work with is to tailor your resume to each role you apply for.
Yes, I literally mean each job.
Have a look at the job description, discover the important thing words and phrases, and insert them into your resume.
I do know I just said to not spam a great deal of programming languages and technologies, but that will not be what I’m suggesting here.
I’m asking you to be tactful with the tools you add, so you’ll be able to explicitly showcase that you will have the precise skills the corporate is after.
You should optimise your resume as much as possible against the ATS (application tracking system) to avoid any unnecessary auto-rejection.
I do know this sounds boring and a number of work, but that is what you might want to do if you must get a job in today’s competitive market.
Networking & Referrals
If I could provide you with only one “hack” to land more interviews, it might be to get a referral.
In response to this post:
The leverage you get with a referral is solely crazy.
We live in an era where individuals are socially awkward and are so afraid of rejection that those that do have the courage to ask for a referral hold a golden ticket.
It is best to start with the low-hanging fruit. I’m 100% certain that somebody in your loved ones or friend group works at an organization where they may refer you.
Often, the one thing standing between you and an interview is the straightforward act of asking.
Please stop reading this immediately and write down 10 of your closest friends or relations, together with where they work.
Then check each company to see in the event that they are hiring for a knowledge scientist role, and ask for a referral.
Sounds easy right? That’s since it is.
If for some reason you’re that weird outlier that has no connections in any respect, which I highly doubt, then you might want to actively construct your network.
LinkedIn continues to be criminally underutilised by most job hunters. What other platform gives you access to individuals who work at firms you must be at and lets you interact with them?
It is best to aim for ~50 LinkedIn connection invites per week to people at your goal firms.
Make certain you send a thoughtful and private connection message, but you don’t should be spending greater than quarter-hour per message.
A fair higher approach is to attach with people you will have an “affinity” with, akin to those that share your university, hometown, or common interests.
Individuals are rather more more likely to connect with you when you share similar traits or backgrounds; it’s baseline human psychology.
Construct rapport first by asking about their experience; when you’ve established a connection, share your credentials and ask for a referral.
It’s a numbers game, so don’t be discouraged if most individuals don’t reply.
At all times Follow Up
Most individuals think that after you will have applied for a job, your work is finished.
Time to kick up your feet and have a beautiful coffee whilst you wait for a response in your application.
Oh boy, do I wish life were really easy.
For those who are doing what everyone else does, you will get the identical results: only a few interviews.
After you will have submitted your application, find the hiring manager, talent partner, or recruiter linked to that job posting.
You will discover their LinkedIn profile or email; it doesn’t really matter.
Then message them something like this:
Hi [name],
I just saw this data scientist role from you guys are I’m very all for applying (or have applied).
I actually have been working as a Data Scientist and Machine Learning Engineer for over 4 years across insurance, e-commerce and logistics within the the classical ML, pricing models, forecasting and optimisation domains.
I might like to have a conversation concerning the role!
Let me know if there may be the rest I should do.
(Obviously tailor it to yourself!)
What you will have just done is put yourself front and centre of their mind for the job. That is somewhere you clearly wish to be.
When I actually have done this up to now, in the event that they reply, you’re almost definitely getting an initial interview.
Mix this step with a referral and you’re golden for getting an initial interview.
Mock Interviews
Walking into an interview without preparation is like taking a driving test without ever getting behind the wheel.
You’re simply setting yourself up for failure.
Mock interviews are the last word “cheat code.” They can help you over-prepare, which is precisely where you must be in case some curveballs get thrown your way in the course of the actual interview.
It took me a protracted time to grasp the facility of mock interviews. I flopped several early interviews that, in hindsight, I must have easily passed.
Today, I cruise through the method due to sheer volume of practice I’ve put in.
Because data science and machine learning roles aren’t as standardised as software engineering, the interview process can feel just like the “wild west,” with many variations.
To cover your bases, you need to run mocks for:
- ML/DS Theory — Testing your foundational knowledge.
- Pair Programming — Live coding under pressure.
- Behavioural — Polishing your “soft skills” and storytelling abilities.
- Case Study Presentations — Communicating technical things in a digestible manner.
This will likely sound like a number of work, and it’s.
The vast majority of people think getting a knowledge science job is a walk within the park; that’s why they get zero results and begin to “blame the market”, and take no personal accountability.
For those who follow the steps in this text, you’ll eventually land a knowledge science role.
Nonetheless, if you must speed run the method, then I invite you to affix the Data Science Launchpad.
That is my coaching programme, where you’ll get direct support from a community of like-minded individuals and me, together with a proven step-by-step framework for landing data science jobs.
You’ll be able to apply to the Data Science Launchpad using the link below:
https://coaching.egorhowell.com
One other Thing!
Join my free newsletter where I share weekly suggestions, insights, and advice from my experience as a practising data scientist and machine learning engineer. Plus, as a subscriber, you’ll get my FREE Resume Template!
Dishing The Data
newsletter.egorhowell.com
