What Took Me So Long to Land a Data Scientist Job

Learning all you need to learn about data science is only part of the adventure. Landing that first job is another. While it might take a while to get your foot into the door, there are several key efforts you can do to shorten this time as much as possible.

Photo by Mārtiņš Zemlickis on Unsplash.

I started my data science journey by completing the IBM Data Science Professional Certificate on Coursera. It took me almost two years to land a data scientist job.

It took me longer than I anticipated to find a job in this field. After spending so much time looking for a job, I think I know the reasons that extended my journey and I would like to share them with you.


Got interviews?


The first and foremost problem was to show I actually know stuff. Without prior job experiences, it is hard to demonstrate your skills. In most cases, I could not even pass the first step and get a technical interview. I felt like HR professionals did not take my resume into consideration due to a lack of job experience in the field.

I got very few technical interviews, and I mostly did well. It made me happy and motivated not to fail technical interviews. It was a good sign that shows I was on the right track and improving my knowledge.

However, I had a hard time reaching the technical interviews. An efficient solution to this problem is obviously networking. If you know or meet people who work in the field, the chance of getting at least a technical interview substantially increases.

If you do not have prior job experience in the data science field, the best alternative is to complete projects. I do not mean the ones you can do in a day or two.

You need to frame a problem and design your approach that aims to solve the problem using data. Then, you implement your approach. Your project does not have to accomplish the task or solve the problem. However, how you think analytically, approach a problem, and use data as a solution matter a lot. I know it is very difficult to do such a project, but you should at least try.


Got quality time?


Another problem for me was not having enough time. I had to keep my job (not related to data science by any means) during the two years I studied data science. So I had only evenings and weekends to study.

It is not just about having time. You need to have a fresh mind to study and actually learn new concepts and skills. If you plan to work while you learn data science, keep in mind that it is not going to be easy.

The whole process becomes relatively easier if you can dedicate yourself to learning data science. You will probably reach your goal faster if you can afford to quit your job. However, it is not an option for some people, including myself.


Hello World!


Software or programming is a fundamental skill for data science. It is not enough to have a comprehensive understanding of statistics, machine learning models, or a great analytical mind. You need to be able to use the tools and software packages to implement your solutions.

I did not have any prior programming experience. I even had to take the “Introduction to C++” class twice to pass. It is not because it was very difficult. I just did not have an interest in programming. I studied electrical engineering in college, and I did not think that I would ever need programming skills. I was clearly wrong.

It took me a while to obtain programming skills. I’m not talking about just Pandas and Scikit-learn. You will need a lot more than that. I think SQL is a must. You also need to be really good at a programming language, preferably Python or R. Git, Spark (or PySpark), Airflow, Cloud Computing, Docker are some of the other tools you need to get familiar with.

Thus, if you do not have a programming background, it will take you a while to get things going in terms of software tools and packages.


Got enough jobs?


The number of data scientist jobs was limited in where I live. This can be considered an environmental issue, but it definitely has an impact. When I first started learning data science, the number of job openings was not many. In the past two years, though, the number of companies that look for data scientists has greatly increased. Thus, just considering the number of open positions, my chance of getting a job is higher than it was two years ago.




Although it took me two years to start working as a data scientist, I’m more than glad that I decided to make a career change to become a data scientist. My motivation and enthusiasm have increased since I started working as a data scientist.

To work in a production environment with real-life data is something we cannot achieve with certificates, courses, or tutorials. It has been a long and exhaustive journey for me, but it is definitely worth it.

Original. Reposted with permission.