17 Reasons Why You’re Getting Rejected for Data Science Jobs
Data science jobs are abundant, and the numbers are growing. Despite the influx in demand, some data professionals are still struggling to find a job. If you’ve filled out numerous job applications you feel you’re easily qualified for, only to be rejected, you’re certainly not alone.
In this article, we’ll go over some of the most common reasons hiring managers might be saying “no,” and we’ll offer you the solutions to start hearing “yes.”
- Overly ambitious job applications
- Failing to leverage past experience in your job search
- Not being proactive in getting experience
- Companies unsure what they want
- Ignoring the job description
- Neglecting a cover letter
- Getting rejected by the ATS
- Your resume is too generic
- Mistakes on your resume
- Job-hopping and/or unemployment gaps on your resume
- You don’t have a degree or “years of experience”
- You lack a powerful portfolio
- You’re silent about your expertise
- Neglecting networking
- Not selling yourself
- Falling short in the interview
- Not a good culture fit
Overly ambitious job applications
Not all data science jobs are created equal. While data science roles may have a lot of overlap, some have radically different requirements.
If your applications are getting constant rejections, there might be a mismatch between your qualifications and the roles you’re applying to. It’s important to understand where you are in your career and how that relates to the expectations of the roles you’re applying for.
If you can’t seem to land your preferred position, try applying to related-but-different jobs that can serve as a stepping-stone toward your ideal position.
“Data scientists,” for example, are some of the most widely advertised job positions, and they have the highest density of master’s and doctoral degrees of any role in the data science field.
Data analysts and data engineers, on the other hand, are much more diverse in their education demographics and are more open to entry-level applicants. If your goal is to become a data scientist, consider taking on a lesser role to begin and work your way to the goal. Especially early in your career, it’s more important to get your foot in the door than to land your dream job right away.
Migration between different roles in data science is a common occurrence, so taking on a “junior” position or internship at the beginning of your data science career could be the key to unlocking future career growth.
Failing to leverage past experience and job history in your job search
Practically every industry is investing in data science. This has opened a world of opportunity for people with data science skills, many of whom have left their previous industries to acquire those skills.
While data skills are the focus, it can be advantageous to remember where you came from.
Leverage your past work experience and combine it with your current data science skills to increase the likelihood of a hiring manager saying “yes.”
If you have previous experience in the medical field, consider prioritizing applications to roles like “biotech analyst” instead of “financial analyst.” And don’t forget to spend time discovering the aspects of your previous career that can add value to your new one!
Not being proactive in getting experience
In data science, you must demonstrate your ability to fulfill the role you’re pursuing. Even getting an entry-level position will require a base level of knowledge and experience.
If you apply to a job with little to no actual work to back up the skills you listed on your resume, your application will easily fall through the cracks.
Don’t wait around for opportunities to fall into your lap—be proactive and seize any opportunity to exercise your data skills. It’s this kind of motivation that hiring managers are looking for in prospective employees.
Even if you’re starting from zero, there are things you can do immediately to begin acquiring meaningful and valuable experiences.
- Reach out to nonprofits and small businesses—you can even offer to work for free. This will give you opportunities to tackle real-world data problems and provide you with working experience to discuss during interviews.
- Take on an internship, paid or unpaid. This will provide on-the-job training and mentorship from more experienced professionals.
- Freelance if you can. If you’re the entrepreneurial type, you may want to consider taking on data jobs as a freelancer. You’ll develop your skills, build a portfolio, gather references and testimonials, and make a little money along the way!
- Do your own projects. If you can’t find anybody to help, you can conduct your own cool data science projects!
Companies unsure what they want
While countless businesses understand the need for data science, not all know how it works, what they need, or who needs to do it. Because of this confusion, companies will often create job listings that are overly broad.
For example, they’ll ask for a “data scientist” when they actually need a Python expert. These job listings may solicit many applications, but most will not meet the company’s needs.
To make the best of this situation, you’ll have to go the extra mile. First, read the job description carefully to determine what the company needs. Then, if it’s unclear, personally reach out to the job lister and inquire about the position.
Not only will you discover more concrete information about the job (allowing you to create a more targeted application), but you’ll also stand out in a crowd of resumes just as generic as the job listing.
Ignoring the Job Description
Nobody likes job-hunting, and with the ease and convenience of online applications and “1-click Apply” buttons, it’s tempting to fill out dozens or even hundreds of applications with hardly any thought. Unfortunately, if you’re doing this, you’re setting yourself up for rejection.
By neglecting proper attention to the job description, you open yourself up to embarrassing mistakes. And you risk wasting time applying for jobs that you’re not qualified for, or may not even want. You also limit yourself to a copy and paste resume, which hiring managers know how to spot.
Pay attention to every job description you read. Take careful note of the precise skills and abilities the job requires, and use those exact keywords to fine-tune and customize your data science resume before applying. This targeted approach to crafting your resume for each job will give you the best chance at getting a callback.
Neglecting a cover letter
For many jobs, a cover letter is still mandatory. Yet, many applicants see it as an inconvenient formality. For that reason, job seekers often copy and paste the same cover letter to every job they apply to.
In reality, your cover letter is a unique opportunity to distinguish yourself and make an impression. It offers flexibility that your resume does not. If you’re given that golden opportunity and your cover letter falls flat, your chances of rejection increase.
Take full advantage of the cover letter to explain how the skills on your resume produced favorable results in your career. If you’re switching careers, this would be a perfect opportunity to bridge the gap between your previous industry and data science.
Don’t just explain what you do; explain how it has helped others in the past and how you can provide value in the future.
Indeed offers a great example of a data science cover letter that could serve as a reference point for your job search.
Getting rejected by the ATS
Today, many businesses use an applicant tracking system (ATS) to vet and filter job applications. As a result, an algorithm could reject you before a hiring manager even has the chance to look at your resume.
An ATS is programmed to look for specific targeted keywords and phrases to determine which applications will make it through the filter into the hands of hiring managers. Here are a couple of tips to help you get your data science application past the ATS:
- Leverage online tools to help you find the right keywords to use on your resume. ZipRecruiter offers a great selection of data science keywords.
- Avoid using abbreviations. The ATS is often looking for terms spelled out. You can never go wrong using the same terminology found in the job description.
- Ensure your resume is submitted as a Word document or PDF to allow the ATS to scan it properly
Your resume is too generic
There’s stiff competition for most data jobs, especially for entry-level positions. So an overly broad and generic resume doesn’t stand a chance in a highly competitive job market.
Don’t follow the herd. If you’re going to land a competitive role, you have to make yourself stand out.
When constructing your resume for a job application, keep three things in mind: specificity, outcomes, and achievements. These three things will allow you to distinguish yourself from every other applicant applying for the same position.
Be very specific and direct about your skill sets that are critical to the job. Explain how you’ve used them, with specific companies, on specific projects, producing measurable results. Be sure to include any live projects that hiring managers can check out to further demonstrate your skill.
You can also include concrete and complementary skills that indicate your data expertise, even though they aren’t essential to the job in question. This will set you apart from other applicants. Consider mentioning the following high-value skills:
- Cloud computing
- Microsoft Azure
- Jupyter Notebooks
- Deep learning
Specifics will differentiate you from other applicants, many of whom will remain in the realm of the “generic.”
Mistakes on your resume
This point is straightforward and applicable to any job application in any industry. However, one of the hallmarks of a data professional is attention to minor details, so the judgment will be less forgiving.
If you make spelling, grammar, or continuity mistakes on your resume, you display a lack of attention to detail, and your application will go in the rejection bin.
Proofread your resume every time you submit it. You should also use a tool like Grammarly to check for grammar, spelling, and even the tone of your writing. It should be brief, powerful, factual, and confident. You could also submit your resume to another set of eyes or even hire a professional resume writer to assist you.
Job hopping and unemployment gaps
Many companies consider it a “red flag” when they receive a resume that shows frequent job changes or long gaps between jobs. While it’s not necessarily a deal-breaker, if either of these are on your resume, there are a few things you can do to make the best of it — or even use it to your advantage.
Luckily, frequent job changes and gaps in employment don’t carry the same stigma as they used to. If you can explain how they contributed to your current suitability for a job role, they can be beneficial!
Here are a few things to consider if your resume includes either frequent job changes or periods of unemployment.
- Don’t settle with, “During this time, I wasn’t employed.” Instead, discuss what knowledge you gained and what new skills you developed when you were between jobs.
- Emphasize your willingness to explore and try new things, as well as your ability to adapt and acquire new skills quickly
- Focus on the present. Showcase the desires, knowledge, and skills that have brought you to apply for this job.
You don’t have a degree/ “years of experience”
Now might be the time to dispel a misconception. Data science jobs are indeed multiplying, and there’s a shortage of qualified labor. However, it’s a common misconception that because of that labor shortage, someone fresh out of college can simply apply for a data science job with a single bootcamp certificate and expect to get hired. While this scenario is possible (and becoming more common), it’s still not a given.
The vast majority of data professionals have at least a bachelor’s degree in a relevant field, and in some positions, master’s and doctoral degrees are commonplace.
This isn’t to say that you need a formal education to get a data science job. In fact, many Dataquest students who go on to get data jobs don’t possess relevant degrees. However, it does make things slightly more challenging.
Start learning and never stop. Whether your style is self-teaching with online courses, learning on the job, bootcamping, or considering formal education, it’s essential for a data professional to constantly learn, acquire additional skills, and stay up to date with the latest trends and best practices.
Join the data science community on GitHub and Kaggle. Learn from more experienced professionals and find a mentor if possible. The key is to develop trade knowledge, not to have certain letters after your name.
Suppose you’re able to demonstrate the necessary knowledge despite the lack of a degree. In that case, the right company will recognize your value and potential and be willing to invest in your future. This suggestion is one of the main reasons why students love Dataquest—it gives students a chance to complete real data science projects, which helps build their portfolio to show future employers.
You lack a powerful portfolio
A stellar portfolio can be your greatest asset in job hunting, and it can help you overcome many, many obstacles. Conversely, neglecting to build a suitable portfolio is crippling, especially for someone new to the field.
Even if you are perfectly qualified for a certain role, you’ll be virtually invisible if you don’t have a portfolio demonstrating your skills and knowledge.
This cannot be emphasized enough: to get a job in data science and progress in the field, you must demonstrate your qualifications.
If you have skills, you must show them in action. It’s the difference between telling a hiring manager, “I’ve had one year of data science experience” and “here are 10 live projects I’ve developed in the last year, here are the results of my work, and here’s what my clients said about me.”
We have an entire article about building a portfolio of your best data science projects. And not just Kaggle projects, but real-world data projects with meaningful metrics and measurable results. Kaggle projects can help but shouldn’t be your primary source.
You should have an up-to-date portfolio on GitHub because it’s a go-to destination for data science hiring managers. If you don’t have a website for your brand, treat your GitHub profile as your billboard targeted to potential employers. This should be the one place you send people to show your qualifications for data science roles.
Show them what you’ve got!
You’re silent about your expertise
Just like how a project portfolio demonstrates proficiency in hard skills, there are also ample opportunities to elaborate on your proficiency in knowledge and understanding of data science concepts and theory.
There are many data professionals out there with impressive portfolios. Still, those who share their work and a constant flow of valuable commentary and insight set themselves apart. So if you want to give yourself a serious boost in the eyes of a hiring manager or recruiter, start sharing your knowledge and experience with the world.
Consider blogging about data science and accumulate a portfolio of articles to showcase your knowledge to employers. You can also write papers or create presentations documenting your successful projects, and give commentary, explanations, tutorials, or advice. In doing so, you’ll continually grow, learn, and provide hiring managers strong evidence that you have the skills and know-how they need.
Should you consider putting your expertise out there, here are some topics you might want to consider covering:
- Data mining
- Big data platforms
- Data visualization
- Machine learning
It’s easy to underestimate the importance of professional networking, but it’s hard to overstate it.
In the competitive field of data science, you can get drowned out by the hundreds of other data professionals applying for the same positions. However, a recommendation from an influential connection is a powerful asset.
Networks like your life depend on it, both offline and online. Even if you’re not currently looking for a job, it’s valuable to keep in touch with friends and colleagues in the data science field. Reach out to other professionals in companies you’re interested in working for. You could even ask them for an informational interview or a good word.
Join the data science conversations, especially on LinkedIn, Twitter, GitHub, and Kaggle. Your LinkedIn profile is the equivalent of a resume for some hiring managers. Sometimes, having the right connections will open doors previously closed to you.
Not selling yourself
Whether in a cover letter, resume, or interview, the objective is the same: convey your value to the company and convince them to choose you instead of someone else. Unfortunately, it’s difficult for many people because we can sometimes be the worst judge of ourselves and are often overly critical of our work.
For this problem, you’ll have to dig deep and discover the art of salesmanship, which comes more naturally to some than others. But these are key principles that you can use for the rest of your career to help secure new roles and progress in data science.
There are a few fundamental principles to keep in mind when considering how to present yourself to a potential employer:
- Be precise about your skills and how that can benefit a company.
- Be specific about your experience, accomplishments, and achievements.
- When discussing a specific project, provide names, dates, and measurable results whenever possible.
- Don’t focus just on “the stuff you do.” Instead, focus on how “what you do” creates value for the employer. Thus, the ultimate question isn’t “What can you do?” but “How will you be a good addition to our company?”
Falling short in the interview
Not many people enjoy interviews, and even fewer are pros at them. If you’re driven and skilled enough to get the long-awaited interview, you absolutely must go in prepared.
Nailing the interview may be the last hurdle between you and starting your new role as a data professional; make it count!
Here are a couple of tips to help you get ready for the important interview:
- Research the most common questions asked during data science interviews and practice your answers
- Outline questions to ask the interviewer to indicate your interest in the job or company
- Request mock interviews from family and friends, and ask for their feedback. It may even be helpful to film yourself during these mock interviews. While it might be painful to watch at first, it’ll give you a broader perspective and help you master the art of the interview!
- Lastly, you simply need to practice. There’s no substitute for the learning experience and developing good communication skills.
Not a good culture fit
The last reason you might not be getting interviews may have nothing to do with education, experience, skills, or qualifications of any kind.
More and more, businesses are focusing on creating their “company culture” to maintain morale in a particular environment. And since not all companies and people are the same, not everyone will be a fit.
While there’s not much you can do if you’re judged, “not a good fit” in the company culture, you can still use this to your advantage.
If companies are placing more emphasis on “company culture,” so should you! Discover what kind of culture is right for you, and seek out those companies that communicate the proper vibes. If it’s a good fit, you’ve got an edge over other applicants.
If there are companies that are eager to hire people whose culture is different from your own, other companies are just as eager to hire someone like you.
You can also attempt to understand the company’s culture you’re applying to and then adjust yourself accordingly. However, you must still be honest with yourself and what you want.
If there’s one takeaway from this post, it’s this: if you want a data science job, you must demonstrate your data science expertise.
If you have the hard skills, show them in action. If you know the trade, put your knowledge on full display. We’ve provided many ways to help you do just that. Of course, you can do all of them or just a few. However, if you’re doing none of them, you risk your applications underperforming when you have everything you need to make them shine.
With Dataquest, you can acquire and develop all of the knowledge and skills you need to take the crucial first steps as a data professional. Join over a million learners who have used Dataquest to develop and hone their career skills and have seen an average salary increase of $30,000. Start for free today!