Hustle Hub #15

Hey friends,

Before diving in to tell you why I rejected a data scientist job, let us take a step back and try to answer another question — Why become a data scientist?

Data Scientist was labelled by Harvard Business Review as the sexiest job of the 21st century and has been chosen as the best job in America, three years in a row according to Glassdoor.

All these attractive job prospects seem to point to a single direction that many people want to go after — and we all know — for some good reasons.

Regardless of the common notion, if you’ve been following my learning journey in data science, you’ll understand why I decided to become a data scientist and how I made my transition — all because of the sweet intersection of academic background, passion and skills, working experience, and job prospects.

Probably you might be wondering now:

I hope this issue would answer the question by sharing my experience and giving you a glimpse of my riding journey and adventure in the data science world.

Let’s get started! 🚀

 Sometimes, Job Title ≠ Job Nature

The importance of a job title differs for everyone due to different career goals.

Similarly, the significance of a job nature also differs for everyone due to different life goals.

Therefore, having a perfect alignment between a job title and desired job nature might sometimes not be the case, putting many job seekers in a dilemma where they have to make their choice — and not surprisingly, I was one of the job seekers.

🙊 Applying for Data Scientist Jobs

Long story short, I applied for various data scientist jobs in different companies when I got started in data science. As expected, most of the time I got rejected to a certain point where my inbox was filled with emails like:

Or…

I was frustrated, but NEVER gave up. I kept learning and improving my skills.

Just kept grinding.

And finally one day, I received an email from the application submitted on LinkedIn to schedule an interview with me.

I was so ecstatic and did a hell lot of research on the company to see how I could match my skills and experience with the job description and the company’s culture.

So the job description required an absurdly wide range of technical and non-technical skills and a certain period of experience that covered various industries. The responsibilities basically included from top to bottom for data and non-data related work, which meant the candidate got to juggle multiple roles while still being able to meet the job expectations.

Simply put, in my opinion, the job description was outrageous and required at least 3–5 years of experience for the entry-level position.

Well, I probably did not meet at least 70% of the job requirements but still, I went for the interview with the firm belief and confidence that I could add value to the company (with my skills and experience) and learn on the job at the same time.

⭐️️ Choosing Job Nature over Job Title

To my surprise, the 70% of the job requirements that I was so scared of not being able to meet were not in the actual job scope, at all.

My only job scope was to build dashboards for different companies (clients) for visualisation purposes — without data analysis or anything. Of course, data visualisation is an important part of any data science process, but the job nature did not really fit what I really wanted to do on a day-to-day basis (which I also mentioned in one of my posts):

More shockingly, I was baffled by the stark contrast between the job description and the actual job scope given by the company.

I was given a job offer as a Data Scientist after the last round of interviews.

During the same period of time, I was also offered a Research Engineer role in another company, with a more well-defined job description and the actual job scope suited exactly what I wanted to do to develop my passion and skills.

Remember the dilemma between a job title and desired job nature that most job seekers face?

I chose the latter.

📍 Final Thoughts

For me, the job title is temporary, but the job nature — the work that really interests and challenges me as well as the valuable skills and experiences learned along the journey — outweighs all.

Till now, I’ve been enjoying the learning journey despite the challenges and obstacles along the way. Every day is never the same as it is another day to learn new things, and I really like to learn new stuff!

Thank you for reading. If you’ve ever encountered a similar experience as I did, I hope to let you know that it is perfectly fine to be in a dilemma (most people do), especially when you’re just starting out in the data science world.

As always, if you have any questions or comments feel free to reply to this issue or you can always reach me on LinkedIn. Till then, see you again next week! 😀 

🚀 Whenever you’re ready, there are 4 ways I can help you:

1. Book a coaching call with me if you need help in the following:

2. Promote your brand to ~1000 subscribers in the data/tech space by sponsoring this newsletter.

3. Watch my YouTube videos where I talk about data science tips, programming, and my tech life (P.S. Don’t forget to like and subscribe 💜).

4. Follow me on LinkedIn and Twitter for more data science career insights, my mistakes and lessons learned from building a startup.

That's all for today

Thanks for reading. I hope you enjoyed today's issue. More than that, I hope it has helped you in some ways and brought you some peace of mind.

You can always write to me by simply replying to this newsletter and we can chat.

See you again next week.

- Admond