By Fatima Haroon
October 30, 2022
Data science jobs are increasing due to the influx in demand. Despite this increase, many data science professionals struggle to find a job. If you are just getting started with data science field, the chances of you filling numerous job applications are high.
You may feel qualified enough for the job but are facing rejections - why? This blog post will review some major reasons why you may not be hearing back from recruiters.
Major Reasons Why You’re Not Hearing Back from Recruiters
You may fear not being good enough or feel like this field is impossible for you. But that’s not always the case. If it is about you, you can easily change things. Determine who you are, what you can offer, and what you’re looking for. Identify your skill set and determine the gaps. Work on closing these gaps.
If your skill set isn’t the problem, then you must understand the job market, be realistic, and learn about the hiring and interviewing process. And the best suggestion is to seek professional help.
1. Your Resume is TOO Simple and Lacks Details
The data science and machine learning fields are highly competitive. So, a generic and broad resume won’t stand a chance. An ideal resume has three things:
- Specific structure (short and crisp)
- Outcomes
- Achievements
A crisp and direct resume highlighting your skill set will differentiate you from the rest. Use specific information and measurable results to woo the hiring managers and demonstrate your skills.
We recommend that you get your resume reviewed by a professional in the data science field. A mentor will help you correct your resume and make it stand out from others, increasing your chances of scoring data science roles.
2. ATS Rejecting You
Many companies now use ATS (Applicant Tracking System) to filter the suitable applications out of the hundreds they receive. So, an algorithm can reject you even before the hiring manager has a chance to review your resume.
A mentor that drives results can highlight the keywords and phrases your resume lacks. Later, you can review the results by scanning the resume on online software. This will help you identify the missing keywords, which you can include later in your resume.
3. Lack of Portfolio to Back Your Skills
Having a stellar portfolio is the best step to making a name in your data science career path. In fact, a portfolio is your safest asset that helps you overcome multiple job-hunting obstacles. Conversely, if you are new and don’t have a portfolio, your chances of getting an interview call are slim.
It doesn’t matter if you feel you have the perfect data scientist qualifications for that specific role; having no portfolio will make you invisible. If you have skills, you need to show them in action.
Maybe you have the perfect portfolio. You simply haven’t utilized it to match your skill. If that’s the case, create a stellar, updated portfolio on GitHub. Your GitHub profile will act as your brand identity to seek potential employers.
4. No Technical Experience
You must be wondering, “I don’t even have any relevant experience. What should I do?” The data science requirements include having a portfolio. Why would a recruiter hire you if you don’t have experience at all?
You can’t claim to be an expert without technical knowledge that backs your skills. So, you must start working on projects to enhance and polish your skills. Pick problems from the internet or on sites like Kaggle. Free data sets are available for you to work on. So, get started right away!
5. Gaps in Skill Set
Maybe the data analytics jobs you are applying to aren’t the right ones according to your skill set. Or maybe you simply lack the required skills. If that is the problem, you should work on these gaps.
Closing your skills gap is critical for you to succeed in the data science industry. Get in touch with a mentor who will analyze your current skill set and identify the skills you lack. This mentorship will guide you on the right path. Your instructor may even recommend some paid and free data science courses.
6. Your Networking Skills are Weak
Never underestimate the power of networking. Having influential people in your network is like experienced, important people vouching for your skills. A recommendation from one of these influential people can give your career the push it needs.
So, start networking - offline and online! Stay in touch with your friends and colleagues in the data science field. Reach out to professionals of the companies you have an eye on. Absorb knowledge offered by these professionals and give insightful reviews on them.
Share a piece of your knowledge and have a constructive debate with these professionals. You will learn a lot and may even impress these influential people and score a job. The possibilities offered by networking are unlimited!
7. Your Job Search Strategy is the Problem
Applying to all jobs and not hearing back from them can be a hassle, especially if your job search strategy isn’t effective. Stop filling your skill set on a broad spectrum. Determine what kind of data scientist you are.
Niche down as much as possible. Apply only to those niche-specific jobs. A more specific field according to your skill set will increase your chances of hearing back from the hiring managers.
The Bottom Line
Facing job rejections can be a morale-downer. If possible, hire a mentor to help you build your technical and soft skills. They will help you start your career from scratch. You can directly access all the juicy news and latest trends in the data science field with the help of your mentor. So, this extra help will be worth it.
Fatima Haroon
Fatima is a professional writer with 5 years of experience in the SEO Blog Writing and Copywriting industry. She uses industry best practices to write converting and compelling copies for global clients. In OFallon Labs Fatima supports the clients with identifying the key pain-points of our clients and create original content based on our offered solutions.