Job search mistakes: Introduction
In the past two months, I've got a chance to help a dozen job applicants with their job search.
I've learned a great deal about their problems: things they were struggling with, areas they can significantly improve, just with a little bit of guidance from someone.
And here are the top 4 common mistakes that you should avoid.
- Applying for a dream company too early.
- Submitted generic resumes which are not customized or optimized.
- Not prepared on leadership/behavioral questions.
- Missing out on good opportunities based on hearsay.
1. Start interviewing a dream company way too early
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Surprisingly it happens more often to experienced job candidates.
A top company's recruiter reached out on LinkedIn, and they had an excellent informal chat.
A week later, the candidate went into a technical phone screen and was asked to write a SQL window function but couldn't remember the difference between ROW_NUMBER and the RANK function.
His code was buggy, and he didn't pass the first round of technical phone screen.
Two days later, he received a letter from the recruiter:
"Sorry, our candidates have to generate bug-free code. I enjoyed talking to you. Good luck, and thank you for your time."
Tip 1: if your dream company contacted you, but you have not done any interview for a while, there is no need to rush it. Don't let the adrenaline decides your next steps.
Ask the recruiter for more time to prepare for the interview and take control of the entire interview process yourself.
Tip 2: when your dream company contacted you for a role, maybe it's a signal that the market demand for a similar position has increased, and perhaps it's time to apply for other companies that were once on your radar but not your #1 choice.
Interviewing other companies before your dream company gives you ample opportunities to :
- Practice and freshen up your interview skills, especially if you have not interviewed for a while;
- Gain leverage in your negotiations:
When your dream company found out you have an offer from a competitor, they are likely to take you more seriously, and you will also be able to use that leverage to negotiate your package in the end.
2. Using generic rather than targeted and optimized resumes
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The generic resume is okay but doesn't have the specific experiences or skillsets a company is looking for. You submitted hundreds of resumes but didn't get any response.
Why?
On average, recruiters only spend 20 seconds glancing through your resume before they decide whether to forward it to the hiring manager or not.
Whether it is a specific data warehousing tool or a deep learning model, if they mentioned that in the job description and you do have that experience, make sure you highlight them and make it easier for them to take notice.
I've written a full article with tips and tricks for resume writing. Feel free to check it out:
https://instamentor.com/articles/5-quick-tips-to-make-your-data-scientist-resume-stand-out-in-2021
3. Not prepared on leadership/behavioral questions
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Tip: Behavioral questions are probably the most frequently asked questions during a job interview, do not overlook them.
In my own experience, I've interviewed many talented data scientist candidates at Apple and Amazon. Some of them are very smart, did well answering technical and coding questions. However, they still didn't get their dream job offers.
Why?
Because they didn't prepare behavioral questions and got nervous during the interview, they came up with mediocre or even stupid answers.
And in the end, it cost them dream jobs!
You can read a full article about how to prepare behavioral questions in a tech company here.
https://instamentor.com/articles/9-behavioral-questions-you-must-prepare-for-a-data-scientist-job-interview-in-2021
4. Missing out on great opportunities because of hearsay
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When I first got an offer from Amazon as a research scientist, many smart friends told me not to take the job.
According to them, the work/life balance is terrible, and people could be very pushy and unfriendly, the turnout rate is high, the culture is awful, etc.
But I believed in my own eyes, my experience with the team during the interview was great, the recruiter was very helpful, the hiring manager was super smart and knew what he was doing.
So I joined Amazon, which became one of the most rewarding working experiences for my career. I not only significantly improved my technical skills, but I also met some fantastic people who became friends that I still kept in touch with after ten years.
Tip 1: believe in your judgment, and don't miss out on opportunities because Jimmy had a bad experience working there. Please take it as a grain of salt, and trust yourself.
Seeing is believing. You will get a good sense of the team and company culture during your interview experience.
Tip 2: let's take a step back. Even if a company may not have the most outstanding reputation, some of its teams may not be the same as the rest of the company. It depends on the team and the leaders of the org.
Tip 3: vice versa, a company with an excellent reputation, may not maintain a good culture throughout the company. Some team may not be as good as others, and it will be up to you to figure it out if that is an excellent team to join or not.
The following are two misconceptions I want to clarify, regarding applying for a job at big tech firms such as FAANG.
5. Big tech == very high hiring bar, and vice versa, small companies == lower hiring bar.
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Companies such as Google, Facebook, Apple do spend a great effort vetting out hundreds of candidates for one final hire, but that does not mean you should not apply.
Here are a few of my thoughts:
- Those big companies have a mature system for hiring. Their hiring machine has a well-oiled engine and lots of support from their HR department. Most of their hiring managers have to go through internal training to learn how to hire the best candidate, treat a candidate with respect, diversify their teams. So it is safe to say you will be getting a formal, predictable process with care.
- Smaller companies (startups, for example) tend to be putting fires all the time. They may not even have a solid, well-documented interviewing process. Their recruiting team probably has much fewer resources and is swamped with questions and emails from job candidates.
- Big companies tend to use more mature technologies, and they are more flexible in terms of your favorite programming language. Compared to a startup, whose first data scientist hire only uses Python and has little or no experience in R, they only want candidates who use Python.
When you found a role that you believe you are a good fit for, don't be afraid and submitting your resume, even if this company is Google.
6. Working at big firms has a significant limit on what you can learn.
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The truth is, big tech firms usually pay people much higher than smaller companies because a majority of their employees' income comes from their RSU (restricted stock units). Since a small company has not IPO yet, you potentially will get paid much less.
Of course, there are expectations. Late-stage startups such as Uber or AirBnB allowed their early employees to vest their stock options in secondary markets, for example. Still, I am talking about it in a general sense.
And my first point is that if a company can pay more for their employees, they can hire more experts.
By simply working with a star team of data scientists and engineers, you can learn a lot.
Secondly, big companies also have internal transfer opportunities. After I built Amazon's first advertising inventory forecasting system, I became more interested in machine learning. I transferred to Amazon's ML team with the full support of my old team and managers.
Lastly, you can manage what kind of project you want to be working on, who you will be partnering with, as long as you have a good story with a good supportive manager (with who I have fortunately met quite a few in my career and forever grateful), even at a big tech company.
Conclusion
Job search is a long and stressful process. To best prepare yourself, you need to sell the best of yourself, avoid common mistakes, practice coding and technical questions, rehearse behavioral questions and answers, and be ready for any behavioral questions.
