Why Did I Reject a Data Scientist Job?
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
By Admond Lee, Data Scientist, MicronTech.
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?
Chances are you may have heard of the profession — Data Scientist was labeled 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. And more recently, IBM predicted demand for data scientists will soar 28% by 2020.
All these attractive job prospects seem to point to a single direction where 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: Why did a person so obsessed with data science reject a data scientist job?
I hope that by sharing my experience in this post would answer the question and give 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 a few months ago. As expected, most of the time, I got rejected to a certain point where my inbox was filled with an email like:
Thank you for your application for the position of Data Scientist at ___. Unfortunately…
Thank you for your application for the position of Data Scientist at ___. Due to the large volume of applications we received, I am sorry to inform you that…
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 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.
To simply put that, 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 values 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, 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 visualization purposes — without data analysis or anything. Of course, data visualization is an important part in 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):
From understanding a business problem to collecting and visualizing data, until the stage of prototyping, fine-tuning and deploying models to real-world applications, I found the fulfillment of tackling challenges to solve complex problems using data.
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, I was also offered as a Research Engineer 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 the desired job nature that most job seekers face? I chose the latter.
Chilling in New Zealand.
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 any 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 in the data science world.
Just take your time to really ask yourself what you hope to achieve in your career, or perhaps, even more deeper, in your life.
Embrace the fact that you might not be able to find the answer to your questions.
Keep asking, keep searching inward and outward, and your choice would be more clear to you, sooner or later.
Original. Reposted with permission.
Bio: Admond Lee is now in the mission of making data science accessible to everyone. He is helping companies and digital marketing agencies achieve marketing ROI with actionable insights through innovative data-driven approaches. With his expertise in advanced social analytics and machine learning, Admond aims to bridge the gaps between digital marketing and data science.
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