The Challenges and Realities of Being a Data Scientist

It may seem that being a data scientist is all sunshine and rainbows. High pay, great benefits, flexible hours, and enjoyable work are some things that come to mind when thinking about a Data Science job.

While these are all true, every job has hidden struggles behind the scenes, and data science is no exception. Don’t get me wrong; it’s a fantastic job, and I absolutely love the field, but not every day is glorious, nor is the whole field perfect.

That’s why, in this article, I want to explore several realities you must accept as a data scientist. Hopefully, this will help anyone reading this post who wants to be a data scientist decide if this field is really for them.

Learning Hamster Wheel

I have said this many times and will say it again, but you can’t learn everything in data science; it’s just too huge!

Not to mention that new research is released every year, in fact, every month, propelling the field forward even more. Just check the Machine Learning category on arXiv; there were 60 papers published on January 22nd!

This leads most data scientists to be on a hamster wheel of constantly learning and staying up to date. This is not a bad thing, but I have had times when it can feel exhausting, as you always feel behind.

I’ve dedicated nearly four years to this field, and without sounding arrogant, I probably put in more hours than many other people, yet I still feel I have only scratched the surface. There so much left for me to learn, which I personally find exciting.

As I just said, if you like learning and keeping yourself sharp, this is not a bad thing. However, along with your other commitments, it can become unsustainable in the long term.

One way to mitigate this is to specialise, which I have started to do with forecasting and optimisation. The problem is that I find everything interesting, so I want to learn everything!

If you enter data science, be prepared for lifelong learning and development.

I have a separate article about how to specialise and the fields available to you.

How To Specialize In Data Science / Machine Learning

Undefined Role

Despite how ubiquitous data science may appear, the job is still unclear.

It’s the classic situation where something is obvious to you but not apparent to others. We may know what data science is, but many other people – in fact, the majority of people – don’t.

This means that data scientists don’t often have precise role requirements for their job. People will ask you questions ranging from getting data to setting business direction. Some people even think of you as just the general "tech guy."

Again, all of this is not necessarily bad, as you learn a lot; however, it does lead to a lot of ambiguity in the title.

In general, the pros and cons are:

The good:

You learn a wide range of skills (data science, engineering, analytics, etc.) as people are unsure what tasks to give you.
Define your own unique role at a company.
This may lead to more responsibility as you are doing all things data.

The bad:

No specialist skillset as you are doing too many things and wearing many hats.
Transitioning to other jobs might be more challenging as your current skills don’t meet their expectations.
Lack of mentorship and risk of being mismanaged if no other data professional is at your company.

This differs significantly from professions like accounting, law, or banking, where people are aware of clear job definitions, systems and specialisms.

There is no structured system for data science, which again is neither good nor bad, and it puts more responsibility on you to progress up the ranks. This can be seen as liberating but also quite daunting. So, it’s up to you if this sort of career suits you.

Competitive Job Market

It is evident that the job market for data science and tech in general is quite difficult at the moment. I have seen this first-hand with the number of messages I receive from people struggling to get a job.

Once your foot is in the door, it is easier, but getting that entry position has become more challenging over the years.

You can no longer just do a certification or a couple of projects and get an internship or graduate position. You have to stand out and put in much more effort than before. You need to have an edge.

I wouldn’t worry too much if you really want to work in this field. With enough work and simply playing the odds, you will get a job. It just takes time, and you need to be patient.

However, if you are 50–50 about becoming a data scientist, then it’s unlikely you will stick out the numerous rejections required to get your first role. I know that may sound harsh, but I am just being brutally honest.

Additionally, even though it is easier once you are in the field, I have noticed that the competition is now much higher as data science matures and there are more seasoned practitioners.

There are data scientists now with 10 to 15 years of experience vying for the top positions. And unfortunately, experience is the trump card in most situations.

What this means is that the competition for top positions is getting even more complex, as it is already for entry positions. I foresee a world where the working hours and intensity will increase due to the pool size of qualified people.

Again, this means you will need to put in more work as you progress to stay sharp; you won’t be able to just cruise through.

Undefined Future

Data science has come a long way; however, there is no clear direction with all the advancements happening constantly. It literally changes every year.

Compared with fields like law, accounting, and banking, which do not often change much, tech space trends fluctuate almost every half year.

This makes it difficult to know precisely what to learn or where to aim for your career long term. The lack of structure and clear plan may bother people as there is a lot of uncertainty in your job.

This is especially true given the rise of GenAI over the last couple of years, which has caused people to fret quite a bit. Just look at what happened this last week with the release of DeepSeek.

The whole AI ecosystem has turned upside down, and there are great parallels with the space race in the 1950s and 1960s. This shows how quickly the field can change in a matter of days.

You can read more about DeepSeek and the stir it has caused here.

Even though I don’t think AI at this stage will take over a data scientist’s job, there is space for some of our jobs to be automated.

I like Yann LeCun’s take on the current standing of LLM’s:

There is a risk that our job will disappear in 10 years, which is far from ideal. However, if this does happen, so will many other jobs.

Overall, data science is not a great option if you want stability and certainty in your career over the years.

Summary & Further Thoughts

Data science, Machine Learning, and AI are all interesting fields in today’s age. However, that interest does come at the price of uncertainty and a constantly changing landscape. What we define as "data science" continually changes as the field advances. As I said throughout this article, this is both exciting and scary. This field would suit you if you love learning new things and thrive with ambiguity. If you don’t like the uncertainty it brings, another job may better suit you.

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