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Do These 5 Simple Things to Make Your Data Scientist Resume Stand Out From the Crowd

These 5 things will make a recruiter look twice at your resume

The Data Science field is currently oversaturated, to say the least.

However, the field isn’t oversaturated with qualified candidates, if that’s what you thought I meant. You may see hundreds of candidates applying for a single job posting, but very few of those candidates are actually qualified individuals who could analyze data if their lives depended on it.

A few years ago I was involved in screening resumes for a position my company at the time was hiring for. I posted the job on LinkedIn, along with a few criteria questions. In the first couple of days, we received about 20–30 applications, but after reviewing the applications, as well as sifting through the ones that LinkedIn filtered out due to their answers to the criteria questions, only two were viable candidates. Extrapolate this experience to those data science postings where 300 candidates have applied, and you can imagine how many of those are actual contenders.

In tech, it’s very easy to call yourself a designer, a software engineer, or a data scientist. In many instances, the meaning of these job titles has changed over time. For example, "data scientist" used to refer to a very senior person at a company who had a Master- or PhD-level Education in the field. In contrast, now, anyone who completes a data science bootcamp may feel qualified to call themselves a data scientist because they understand statistics and can use some data analysis libraries.

All of this means that with all of the noise recruiters have to wade through to get to the legitimate candidates, you need to step up your resume game to stand out from the rest. None of the suggestions listed below are groundbreaking or revolutionary, and if anything, you’ve probably heard of them before. However, once you start paying attention to these details, recruiters will begin paying more attention to you. Here are five things you can do to your resume to make a recruiter look at your resume twice.


What are recruiters looking for in a data scientist’s resume?

  • Learning and growth beyond the classroom: Learning data science in university or through a coding bootcamp is great and all, but these are very sterile environments that only take your skills to a certain level. While many employers know what they’re getting when they hire a fresh graduate, it can be beneficial to impress them with your ability to have gone beyond what you learned in the classroom. Working in data science is unique in that you will be constantly forced throughout your entire career to learn new technologies, apply new skills, and generally roll with the punches (i.e., learning how to work with ChatGPT and other advanced AI models instead of fearing them). Therefore, you might as well get in the groove of lifelong learning early on and impress the recruiters by showing them how you’ve advanced your knowledge already beyond the core fundamentals.
  • Ability to both lead and collaborate as part of a team: There appears to currently be a glut of entry-level data scientists thanks to the popularity the field has received, especially in the last five or so years. That, coupled with a mass exodus of boomer data scientists who decided to retire in recent years, and you’re left with companies scrambling to fill positions at all levels, but especially those in more senior "team lead" or management positions. Not only that, but they’re also having to employ entry-level data scientists who may have never worked as part of a team due to completing their education in isolation during the height of the pandemic. Teamwork and leadership are two things that can’t really be taught (no matter how much they claim they can in those organizational behavior courses you’re forced to take), but you can improve your skills in them by practicing. It can be as simple as entering a hackathon and working as part of a team with people you’ve never met, or taking on a leadership role in a club or volunteer organization you’ve joined. Either way, you can demonstrate to employers that you can work as part of a team, and even lead a team if required.
  • Domain expertise: While anyone can analyze data, only a few can draw meaningful conclusions that can help companies make vital decisions moving forward. This is why it can be useful when transitioning into a data science career from another career (i.e., as an engineer, teacher, nurse, scientist, etc.) to remain in the field and use the domain knowledge you’ve acquired to help companies looking to solve problems in the area. However, domain knowledge can also be acquired, whether by auditing university courses for free, reading books, or attending networking events. Whatever way you choose to acquire domain expertise, make sure you can speak to it under a variety of different scenarios and that you’ve created a portfolio where you’ve put your knowledge to the test in creating personal projects that solve problems in that field (see below).
  • Quantifiable impact: As Ken Jee states in his article Data Science Resume Mistakes to Avoid, your worth as a data scientist is tied to your ability to impact a company positively. In other words, recruiters want to know what problems you’ve solved and what the outcomes of the project were. Did a company optimize its process and increase its earnings after you found a way for it to market to its customers more efficiently? Did you discover a relationship between a nearby coal mine and its effect on wildlife that could initiate discussions on pollution management in your area? Whatever you’ve accomplished, recruiters want to know the details of how you’ve produced impact through the projects you’ve completed.
  • Education and experience: Whatever your background or number of years as a data scientist, recruiters still want to know what your education and experience are in the field. A healthy mix of education and experience is sought after. But don’t worry if you’re a new graduate – you may have developed experience in unexpected places, like a capstone project, an internship, or through a club you were a part of. Additionally, you may have even made your own experience by developing a portfolio, starting a blog or newsletter, or doing pro bono work. Whatever your education and experience, both should have prominent places on your resume.
  • The right stuff: You could be a knockout R programmer, someone who can conduct time-series analysis on penguin population numbers with the best of them, but if you can’t write code in Python, understand business problems in the business sphere, or give recommendations to clients based on well-rounded domain knowledge, you probably won’t be the right fit for certain companies. Recruiters are looking for individuals who have the "right stuff" – people with abilities in the right technologies and the right domain knowledge for that company. This means that you need to tailor the technologies, the skills, and the domain knowledge you acquire to fit a certain type of company. For example, if you’re going to a science-based company, you’ll likely need to be proficient in R; however, if you’re going to pretty much any other type of company, Python will be the standard. Luckily, nearly every industry out there needs data scientists, which means you get to pick a focus that you’re genuinely interested in!
  • A professional portfolio: "Show, don’t tell". As I’ve mentioned before, you can tell an employer all day that you’ve got the skills for the job and that you’ve applied them to solve real-world problems. That’s great and all, but how are they supposed to believe you? I mean, they’ll find out either way if you make it to the technical interview, but you may never even get there if they interview another candidate who can show them definitively that they have the skills and the impact to back it up. A personal portfolio containing projects that solve problems in your target industry will help you convince recruiters that you have the skills they’re looking for.

5 things to make your data scientist resume stand out

1. Tailor, tailor tailor

For some reason, it still amazes me how few people understand the importance of tailoring their resume with how many resources are available online that stress the importance of using keywords and doing your research for each resume you submit.

From experience, nothing quite causes disinterest in a resume like one that isn’t tailored to the company it was sent to. Not only do generic resumes not tell recruiters if you could be a good fit for the team, but they also show a lack of effort and preparation. By all means, I understand the grind of applying to 200 jobs. However, have you ever considered that you may not need to apply to 200 jobs if you took the time to sit down and craft a resume that was tailored to each job you were applying for? It could very well be that you start getting calls for interviews after applying to only 30 jobs because you’ve taken the time to give recruiters exactly what they’re looking for. To tailor your resume effectively, you’ll want to:

  • Keep it as short as possible (one page if you’re a fresh graduate, slightly longer if you have many years of experience)
  • Only include what is absolutely relevant (yes, you may have worked 3 years as a barista, but you may have more relevant and impactful experience to include from when you were part of a club that taught coding to underprivileged kids – however, I should mention that there are some benefits to including non-relevant work experience early on, which could show that you are dependable, trainable, etc., but these merits could be debated all day)
  • Match the language of your resume to that of the job ad (this is mostly important for getting through the all-important screening phase of job application software, but also shows the human who may eventually read your resume that you’ve taken the time to read the job description carefully)
  • Include keywords or phrases that you see emphasized in the job description (see below)

It should also be noted that the importance of keywords doesn’t necessarily have to do with the recruiters reviewing resumes (though they sure do like to see important keywords that have to do with project impact or the technologies you know), but more so to do with the application software companies use to filter out resumes. With few, if any, companies even accepting resumes personally via email anymore, it’s becoming harder and harder to stand out when a piece of code is deciding whether or not to show your resume to the recruiter. As much as this seems like a stupid game to have to play, you’ll have to become an expert in including keywords in your resume to ensure that it even gets the remotest chance of being looked at by a human. The best way to do this is to include keywords throughout your entire resume, such as in your education section, descriptions of past experience, and in the skills section. It’s also not a bad idea to include the most important keywords at the beginning of the resume, which will stand out most to a recruiter.

2. Quantify achievements, experience, and impact

"I wrote a program that analyzed sales trends" is a lot less demonstrative than saying "I developed a program to analyze and predict sales trends using X technology that resulted in improved efficiency in future sales predictions by 20%". The first statement makes a recruiter say "Okay great, so what?", whereas the second statement explains what you did, how you did it, and most importantly, why it was important.

Quantifying your achievements, experience, and impact is an important small step towards solidifying your resume which offers a few benefits. First, it provides results to back up the claims you make. Second, it suggests to recruiters that results are what power and guide your priorities and future performance. Finally, quantification is a great way to stick in the minds of recruiters and stand out from the rest of the competition.

Quantifying your achievements, experience, and impact does take some work to get started, so it’s important to begin setting up your workflow as soon as you begin something new, whether it’s school, work, or a project. Here is the general workflow I’ve used for the past four years to gather the numbers I need to quantify my achievements, experience, and impact:

  1. Track your work: I track absolutely everything that revolves around my work, including my hours, the number of projects I’ve completed, details on what those projects entailed, and the results of those projects. I’ve even logged my hours spent debugging! I find that the Notes app on my phone is sufficient since I typically bring everything over to a more permanent document at the end of each month, but I know plenty of people who have developed systems using Notion or spreadsheets.
  2. Develop some ranges for when exact numbers are lacking: If you’ve been tracking your working data for long enough, you’ll notice times when you don’t have exact numbers. For these instances, develop some ranges for data to indicate the relative amount of work completed while acknowledging that it can vary sometimes. For example, I could say I completed on average 5 data science articles per month during the year 2021.
  3. Focus on the key performance indicators that recruiters love to see most: Money, people, time, and rankings are the four most important metrics to recruiters. Examples for each include stating how much money your project made for a company, how many people you managed within a team, how long you worked on a specific project, or how much you improved a certain ranking for or within a company. It can be helpful to write these things down during a project to help keep them fresh in your memory.

Once you’ve collected and checked your data, you can begin turning them into 1–2 sentence summaries to include in your education, project, or experience descriptions on your resume.

3. Include projects that impart impact

How many people have you talked to looking to get into data science who have created a project that will "predict the stock market"? Just think of how many poor recruiters have had to sift through portfolios with those types of projects in there. Yeah, enough said.

If you’re serious about getting a job in data science and standing out from the 500 other people who want the same thing, you need to include projects on your resume that impart impact. This will be industry-dependent, so it’s not a bad idea to do some preliminary research to see what problems are facing organizations and companies in that industry. It’s important to note that these problems don’t have to be huge, nor do they have to have never been solved before. You could even find an alternative solution to a problem that has already been solved. For example, you could run an analysis that finds that the undefinable anomalies in pollution data are coming from a road construction project that’s kicking up a lot of dust. Alternatively, you could run an analysis that tells a small business in your community that they should be marketing to their customers on Fridays because that incites the most weekend shoppers.

The main point of including impactful projects is to show recruiters that you can find a problem and work your way through to a solution. Project management through an entire project lifecycle is not something that comes easily, so showing you can successfully carry it out will mean a lot to recruiters, especially those who are looking for candidates to hit the ground running in an established data science department, or who are looking for candidates to be the data science department themselves. Further, these types of industry-specific projects are a great way to demonstrate domain knowledge and provide some impact, regardless of whether they get acted upon.

4. Education first if you’re a recent graduate; experience first if you’re not

The order of resume sections can be debatable, but the standard is to put your education first if you’re a recent graduate and your experience first if you’re not. For those that fall in a bit of a grey area where they may have experience in an unrelated field and went back to school to get some form of education in data science, putting education first, followed by an experience section with only the most relevant jobs could be the way to go.

I’ve seen plenty of resume first drafts where people have put their sections in questionable orders that don’t necessarily impart confidence to the recruiter. However, by using the method above, you’re most likely to present that which you are most confident about first. Further, in an interview, recruiters will often begin asking about your resume in the order it’s presented, so it’s always nice to be able to speak most confidently about the first couple of sections of your resume.

5. Make it eye-catchingly skimmable

If you can’t get the gist of your resume in 15 seconds, it’s time to rewrite.

When I was reviewing resumes, my brain seemed to instantly shut down when faced with a wall of words. You may even find the same thing happens to you when you’re reading something online and it’s just a wall of text with no headlines to break up content and make it skimmable.

The most eye-catching resumes that I’ve seen from experience are the ones that are not overwhelming with words. There are always plenty of whitespaces, large headers to break up sections, and bullet points containing only one to two sentences that provide thorough, but short summaries. When the recruiter doesn’t feel overwhelmed or drowned in words, it makes it a lot more pleasant to consider the resume.

Given that recruiters are capable of looking over a resume and making a decision in 7.4 seconds, a good benchmark for you to use is about 15 seconds – if you can’t get the big picture of your resume in that time, you need to make it more skimmable and make the important information easier to glean. Like with this article, I make all of the important points stand out, so you can skim this article in less than a minute, get the important information, and then read the body of the paragraphs if something is particularly relevant to you. You need to do the same with your resume.

Making your resume skimmable can be as easy as using big, bold headings to title your sections, dividing lines between sections, and bullet points to break up the text into readable, easy-to-digest segments. If you’re finding it difficult to include all of the relevant details, you can always pick the most insightful ones, and then fill in the remaining details in your portfolio or the interview. As much as everything should be said in the resume, much more can be said in a portfolio or interview, so the main focus should just be on imparting the details that will make or break a recruiter phoning you for an interview.


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