
I recently ran a survey among data scientists and found out this shocking number – 86% are blindly sending out job applications, and hoping for the best. Hoping is not a strategy, and in times like these, it can be the difference between landing a job or staying in the ranks of the jobless.
You’re not applying to a fast food chain, so stop being generic! The data labor market has turned into a battleground where only those who stand out can succeed. This means you can no longer afford to blend in with the crowd.
You need to stand out, and for that, you need a strategy.
This article is for you if:
- You’re part of the freestyling no-strategy job-hunting community and you’re tired of getting rejection after rejection
- You want to learn how successful data scientists land jobs even in the middle of an economic crisis.
I’ve collected testimonials from successful data scientists and connected with several others to understand their winning job-hunting strategies. In this article, I’ve combined their wisdom and experience into a 3-step strategy to guide you through these challenging times and help you succeed.
No strategy is a failing strategy. So let’s bring you to the winning side!
1. Align Expectations with Reality

When job-hunting, it’s crucial to understand what factors are within and beyond your control. This is key to developing a winning strategy because it helps you focus on the things you can optimize for to better navigate your search.
Factors beyond your control & what you can do about them
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Job Availability: Many positions aren’t publicly advertised. Also, some roles are filled quickly, often by the first qualified candidate. In general, the later your resume comes in, the higher the chance that it won’t get reviewed. This means timing and luck play a huge role. What to do? Try applying to jobs as early as they’re being posted.
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Competition Intensity: High-demand jobs attract thousands of applicants. This means you will be pitted against competitors with more experience. This doesn’t mean they’ll get the job, but they’re more likely to pass the resume screenings. What to do? Networking! It can fast-track your resume to the recruiter’s desk.
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Location Constraints: Proximity to the job location can be a deciding factor, even for remote roles. Companies may prefer candidates near their office or in the same time zone, especially with Return-to-Office (RTO) policies coming back in some firms. So you’re already at a disadvantage. What to do? Clearly state in your resume that you’re ready to move if you are.
Factors within your control & what to do about them
- Expectations: It’s crucial to target roles that align with your current skill level. This will save you the energy from applying for jobs you don’t qualify for. I used to do that a lot thinking it wouldn’t hurt, but it’s just a waste of time + it’s mentally draining. You also need to understand how to navigate the hiring pipeline. You’ll see why.
- Resume: The objective of a resume is to get you an interview. Recruiters and hiring managers are your main audience, so it’s crucial to tailor your resume to appeal specifically to their needs and expectations.
- Networking: Applying alone will get you nowhere. You have to network to better understand the company you’re applying to and get a referral if you can.
The typical hiring pipeline for tech jobs & what to do about it

Again, it’s important to understand how things work out there if your goal is to optimize your approach to it. You need to focus on understanding what the typical hiring process looks like. You’ll quickly get why crafting a great resume can become your boom or doom!
Phase #1: Your resume first lands in an Application Tracking System Most large-scale companies use systems to manage applications. These systems do not reject applications, it’s a myth; humans do. However, the ATS extrapolates specific keywords from your resume looking for those relevant to the role. Many ATSs can even score your resume based on those keywords.
Phase #2: Then if you’re lucky, it gets reviewed by a human Some companies outsource this process to third parties who scan for resumes, typically over 5–20 seconds to evaluate whether or not you’re a good match.
In other companies, recruiters usually search the ATS for specific keywords related to the role they need to fill. This is why you must use keywords from the job description in your resume. You need to craft an ATS-friendly resume above all to get the attention of the recruiter.
The recruiter will then spend a few seconds reviewing your CV and deciding whether or not you’re a good match for the role. In both cases, yours must stand out immediately! The dropoff is usually huge here, with only a few dozen candidates surviving this screening.
Phase #3: Recruiter ScreeningAt some companies, a recruiter might first call you and have a brief exchange with you before moving you to the next stage. I had one at Ubisoft but not Spotify.
The goal of this interview, which is often a phone call is to double-check your motivation and experience. That’s when you want to show how much you’re pumped up!
Phase #4: Technical + Culture Fit ScreeningOnly promising profiles reach this stage. This includes culture fit interviews and coding/business challenges, sometimes more than one.
For technical challenges, candidates usually need to solve them in front of a full-time employee. This usually includes SQL & Python cases, sometimes alongside a data business case. For other companies, you can end up with a take-home ML challenge.
When it comes to the culture fit interview, the hiring manager is usually the one conducting it and deciding whether or not you’re a good match for their team in terms of soft skills, values, and culture. That’s your moment to shine!
The Final Boss: Congrats! You got a job offer It’s not time to let go just yet. At this stage, you want to negotiate and make sure you’re getting a good deal for all your hard work. Don’t be afraid to ask for the package you want, it’s always essential to negotiate, no one knows your worth better than you do. That’s when you can give yourself a hug, not before.
2. A Great Resume Is Your Golden Ticket In

If you’re not getting callbacks from at least 5–10% of your applications and you’ve been applying to roles you should be qualified for, then updating your resume should be your top priority.
I’ve seen accomplished data scientists investing hundreds of dollars in professional resume services to perfect their CVs. That’s how crucial it is to nail the resume part.
Here’s a step-by-step guide to crafting a great resume:
- Stop cold applying; target your applications. Sending out generic resumes doesn’t work – at least not in this jungle crippled by this fierce economy. Pick out 5–6 companies and tailor your resume to them.
- Understand their business & be the person they want to hire. Review their annual reports. Learn about their strategies; what are they investing in? If you spend 5 minutes sending a CV, expect them to spend 5 seconds skimming through it and rejecting you. Why should they hire you? Focus on that!
- Quantify the impact of your work. Always remember each of the bullet points in your resume should answer what you did, how you did and what was the final result. Include things like the number of people on the team, type of data used, number of users, technical stack, etc. Try to squeeze them in a single line or two for each experience you’re describing.
- Use active language. Always active verbs like "Created", "Developed", and "Led" over passive ones. Use ChatGPT to improve your wording.
- The more senior you are the more important your domain becomes. It’s crucial to be specific about your skills. **** For senior roles, like in NLP, just listing general ML experience isn’t enough. Your resume must mention NLP, NLU, and LLMs to pass the initial screening stage. The market is better for more experienced people, but it doesn’t mean it’s impossible for new joiners. You have to do this too to stand out.
- Get your resume reviewed. Ask folks in the same field to review. Ask friends in unrelated roles for their opinions. This helps in simplifying content to make it easier to consume. Again, make use of ChatGPT. Just make sure to give it proper context, something along the lines of: "Pretend you’re a recruiter screening resumes for [insert job title] role, here’s the job description [insert job description], how can I improve my resume to fit this role, making sure my experience uses active language to answer what I did, how I did it while quantifying impact with numbers?".
- Consider getting the Tech Resume.
Job seekers come from all sorts of backgrounds, let yours shine
- If you’re self-taught, emphasize your hands-on experience through practical projects.
- If you’re a graduate, highlight your academic achievements and any internships to showcase your foundational knowledge.
- If you’re transitioning from another career, focus on how your past experiences or studies make you suitable for a Data Science role.
- If you’re already experienced in the field, it’s important to detail your specific achievements in data science.
Your unique path to data science is a strength, so make sure it shines through your job-hunting strategy.
Common resume mistakes you need to avoid
- Not tailoring for the job: Failing to customize the resume to the specific position and its requirements. I said that before, but it’s #1 on the list!
- Hard-to-scan resumes: Overly complex layouts make it difficult for recruiters to quickly find important information.
- Overly flashy resumes: Excessive graphics or colors can distract from the content.
- Inconsistent formatting: Lack of uniformity in fonts, sizes, or styles makes the resume appear unprofessional.
- Excessive bolding: Using too much bold text can overwhelm the reader and dilute the impact of key points.
- Ignoring your audience: Using internal acronyms or jargon that is not universally understood can confuse the reader.
- Messy phrases: Poorly constructed sentences or grammar errors can create a negative impression.
- Long sentences: Lengthy blocks of text or sentences can make the resume hard to read.
- Non-clickable links: Links in the resume should be clickable, make sure nothing is broken in there. Only include links to updated and relevant GitHub, LinkedIn, or personal websites.
- Full URLs in links: Avoid long URLs, make sure links are neat and concise.
Great resumes immediately capture the reader’s attention. They showcase specific, rare, and valuable skills or experiences that the role needs. They’re not generic but tailored specifically for the role you’re applying for. They demonstrate a clear and solid career progression relevant to the role.
To get you started, check out my step-by-step guide to crafting a great resume ⬇ (I show you the exact resume that got me a job at Spotify)
How My Resume Landed Me In The 0.1% Accepted Applicants at Spotify
3. Networking Will Fast-Track You to the Recruiter’s Desk
"Applying alone will get you nowhere. You have to network."
I’m not the only one saying it, it’s a consensus from the data science community. See for yourself.

"My tiny startup opens one job rec – what happens? 700 resume drops in one day."
How do you expect to stand out amidst 700 resumes (that’s just in one day)? The job market is currently flooded with candidates. The demand is still there and it’s growing, but the supply is increasing at a faster rate, hence the "oversaturation".
This imbalance is the reason why it’s become even harder to land a job these days. It’s a common trajectory for any thriving field, data science is no exception. It has simply entered this phase of its lifecycle.
"But you know who got a phone interview immediately? My referral."
Networking isn’t just about who you know; it’s about making connections that can lead to referrals, which significantly increase your chances of getting an interview.
Referrals are the graal of the job market
A friend of mine recently got referred for a highly competitive role at Apple. She simply reached out to an Apple employee on LinkedIn asking for information about the role. Guess what happened? She ended up with a referral. She didn’t even ask for it! That person was kind enough to offer help on their own initiative.

Referrals matter because they almost always guarantee that your resume will be blessed with an in-depth review. At least, it will be seen now. That already sets you in a better place than 90% of applicants.
If you’ve applied to a role within your qualifications, nailed your resume, and secured a referral, your chances of landing on the recruiter’s desk increase significantly.
But getting referrals isn’t straightforward. You obviously can’t just walk to anyone on LinkedIn and ask them for one. They’re most likely not even going to reply because they don’t know you.
This is where honing your networking skills becomes crucial. Many Tech folks might feel uncomfortable with networking, but it’s a necessity now.
I’ve shared my journey of what networking tactics helped me land a Data Scientist role at Ubisoft in a detailed article. Be sure to check it out ⬇
Networking Got Me Hired in Tech Even After I Got Rejected, Here’s How I Did It
Here’s a summary of how to approach networking on LinkedIn
- Check if you have any first or second-degree connections on LinkedIn working at one of those 5–6 companies you’re targeting.
- Reach out to them and see if they’re open to a chat or a coffee meet-up. Many are often open to help. If your network isn’t strong, now’s the time to start building it – it’s never too late.
- Find out what the focus area of the company is from those people. This knowledge is also beneficial for interviews. Interviewers will appreciate candidates who have thoroughly researched the company.

Less qualified people will get the job over you because they embrace the discomfort of networking
In one of my previous articles, I discussed the importance of networking, and it sparked some heated reactions. Many people are frustrated about the unfairness of the job market where networking plays a bigger role than sheer merit.
One of the examples from the comments mentioned a tech influencer who got the job over more skilled candidates. This may seem unfair, but again it’s another example of how powerful networking is nowadays.
I understand that feeling of outrage, but the professional world isn’t always a fair playground. It’s a tough pill to swallow, but making your peace with it is crucial. The key is to navigate the system smartly and not fall victim to it.
If someone with average skills can leverage their connections to land a job, imagine the potential for someone with solid skills and a strong network. Why leave the chance for underskilled folks to outshine you just because they’re better at pulling the social card?
Tech isn’t the hideaway it once was for those who prefer to avoid human interaction. Just like in other industries, building connections and networking have become essential parts of the job-hunting process.
Adapting to this aspect of the job market is essential. You can’t change the rules of the game so all you can do is strategize accordingly. Of course, you have the choice to say no. If stepping out of your comfort zone to embrace networking isn’t to your liking, you can choose to skip this advice and be happy with a 1% success rate.
Final Word + Recap
Right now, I’m sitting at my Spotify desk, and it’s pretty tense. We’ve got a big layoff happening – 1500 people, which is like 17% of us, are losing their jobs. It’s the third big wave of layoffs I have experienced in just 10 months.
Not sure if I’ll survive this one this time. I’m in France, and layoffs work differently here. It can take weeks if not months before I find out if I’ll join the club. It’s a weird and tough spot to be in, but it’s got me thinking deeply about how hard it is to find a good job these days.
I wrote this roadmap with myself in mind too. This is exactly what I would do if my job got axed. I followed this strategy before and it worked. I’ll follow it again if it ever comes to it. There is no other secret to this.
TL;DR
- Align Expectations with Reality: Understand the factors within and beyond your control in job hunting. Focus on targeting roles that match your skill level and optimize your resume accordingly.
- Master Resume Crafting: Tailor your resume specifically for 5–6 chosen companies. Emphasize quantifiable impacts and use active language in your resume. Avoid common resume mistakes.
- Embrace Networking: Recognize the crucial role of networking in getting job referrals and interviews (even in Tech). Build and utilize your LinkedIn connections for potential referrals. Understand company focus areas through networking, enhancing your interview preparedness.
The strategy assumes you’re qualified for the jobs you’re applying for. If you’re not, then go back to studying and try again once you’re ready.
Otherwise, downgrade the scope of your search. I wouldn’t recommend doing that; it should be kept as a last-resort option as I always encourage readers to align their job search with their career goals, and not the other way around.
Keep in mind the job market is recovering, slowly yes but surely, better times are ahead, good luck friend!
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