When I first decided to get into the field of Data Science, I had so much hope and expectation. I mean, when it comes to data science, there is always the premise that the demand in the field is high and hence it "should" be easy to get a job, right?
Not to mention the potential for high-income rates!
People pursue careers in different fields for different reasons; some do it for the joy of working in the field, some do it for the chances of remote work or relocation, some do it for the opportunity to learn or become famous. And, of course, some do it for the money.
Regardless of why you decided to join data Science, you probably had some unrealistic expectations going in, which might have led to a little – or maybe, a lot – bit of disappointment.
Job hunting is always tedious, from preparing your resume to the endless stream of interviews – if you were lucky enough to get some – and the waiting to hear back, which is not always the case.
So, if data science is a field with endless job potential and raising demand, why is it so difficult to get a job in the field? Or – often – get a chance to interview?
Although data science is gaining popularity every day, and it will only get more relevant in the future – considering how dependant our Technology is on data – getting a job in the field is not an easy task.
Why is that?
№1: Companies don’t know what they need/ look for
Let’s start with difficulties due to the hiring companies. Companies hire talents to solve a specific problem they are facing or to increase their revenue.
But, the issue with that is, sometimes the company hiring doesn’t know what will be the correct skill set needed to solve their specific problem. Sometimes hiring managers don’t know what a data scientist is or what they do.
Moreover, when posting jobs, companies list some technical requirements without even knowing how, why, or where they can be used. Due to that, they may attract the wrong people for the job or might even hire the wrong talent.
There is also the common misconception that an engineer with Tableau, some experience in big data, machine learning, Python, and the basic knowledge of statistics is the definition of a data scientist.
№2: The interview process is faulty
Related to the previous point, if the company doesn’t know what skill set they need to solve their current problems, the interview process will be inaccurate and unfair to the candidates with the skills they actually need.
Often the interview process focuses on testing the applicant’s ability in using some specific tools, regardless of their understanding of the underlying science and logic. Where if you forgot one function name in Python, it might be considered a point against you.
This kind of interview process may Work if you’re trying to land a job as a software engineer – even then, I will argue against that, but that’s a topic for another article – but the same rules should not apply to data science roles.
№3: Applicants lack practical knowledge
Okay, let’s be honest; there are a ton of data science-related articles online. If you’re a newbie looking for advice, you’ll probably use Google to get some answers and advice. The problem is, sometimes that advice may be outdated and old.
Data science is a technology field the is rapidly evolving; yes, the basics will remain the same, but some new findings and algorithms are developed regularly. So, how can applicants prepare for a job?
Often, applicants focus on mastering the theoretical aspects of the field. They could have a master’s degree or even a Ph.D. and don’t know who to walk through a data science problem or how to start looking for an answer!
On the other hand, many tech people can show their real-life skills to the hiring team by building applications and solving actual problems.
Practice without theory is as bad as theory without practice. The challenge you’ll have to overcome here is, finding the correct balance between concert theoretical knowledge and valuable practical experience.
№4: Good applicants don’t know how to sell themselves
Marketing, marketing, marketing.
How many times do perfectly qualified applicants walk away without landing the job? You’re probably thinking, more times than you can count.
But, ever asked yourself, why?
Often, highly qualified, skilled academics fail job interviews despite being very aquatint with theoretical knowledge and having conducted impressive research in the field.
When you look closely at those applicants, you’ll realize that they lack a strong portfolio and have trouble talking about their data science experience. The lack of marketing skills is a widespread concern in aspiring data scientists.
Sometimes landing a job is about equal parts talent and marketing.
№5: Difficult to set an ROI
Let’s look at it from a financial perspective (The company’s finance, that is).
Training and hiring talent in data science is a costly process for any company. For them, they need a clear ROI (Return on investment), which means the gain from the investment should be worth its cost.
In the field of data science, the return on investment is not always immediate, which requires companies to be patient with data science projects to see a good return. However, most companies are not that patient.
Many companies often don’t know how to translate various data science solutions into valuable and meaningful real-life use cases.
Takeaways
In general, getting a job is not an easy task; it becomes more difficult when you’re trying to get a job in a popular and in-demand field such as data science. The difficulty of landing a job is not always the result of the applicant not being good enough or having the right skill set for the job.
Sometimes, the companies hiring don’t know what they really need or how to conduct a fair interview process that will eventually lead to them hiring the correct talent.
Regardless of why you’re not landing a job right now, if you focus on building a strong portfolio and having enough practical experience, you will be able to land your dream job.
Just work on strengthening your application, develop a good strategy to market your skills and previous projects, and customize your resume to the job you’re applying for, and remember,
"All things that are worthwhile are very difficult to obtain" – Jonny Kim"
Good luck!