If you are going to apply for Data Science Job in Industry . So I am going to tell you about mistakes that you have to avoid in your Data Science Job Search .
So, what exactly are the most common mistakes that cause people to fail data science job searches and interviews?
Three of the most common mistakes made during data science job searches include :
- Applying to different types of positions simultaneously
- Not preparing for interviews
- Suffering from analysis paralysis
Mistake 1: Applying to Multiple Position Types at the Same Time
The first mistake we will look at is simultaneous applications for different position types . For different position types, I mean positions within the data science field such as data scientist analyst, data scientist algorithm, or a related position such as machine learning engineer.
When I first started in the tech industry, I didn’t realize how different positions such as data science engineering and data science statistics can be. You might think that applying to multiple position types at the same time is a good way to turn the odds in your favor. However, in reality, this is a bad idea.
Mistake 2: Going into an Interview Unprepared
The next mistake that many job seekers make is going into an interview without adequately preparing for it. Some will head into interviews without even knowing what type of questions are likely to be asked.
This mistake is far more common than you might think, and the reason is that people mistakenly think that the job search is a simple numbers game. They assume that if one simply applies to enough jobs, eventually they have to land one .
Mistake 3: Paralysis by Analysis
The last mistake we will talk about is paralysis by analysis. This might ruffle some feathers, but as a data scientist you can’t always analyze everything to death — no metric will tell you what part of the field you should enter.
I totally understand that people care about efficiency and ROIs, but the mistake I want to point out is worrying about whether learning something would be helpful before taking actions to learn.