Top Mistakes to Avoid in Your 2022 Data Science Job Search | by Emma Ding | Jun, 2022
DATA SCIENCE
The 3 Most Common Data Science Job Search Mistakes and Tips to Avoid Them
Do you find yourself constantly striking out during your data science job search? The truth is that you can do nearly everything right throughout the job search process, and yet one mistake could ruin your chances of landing the job.
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
Of course, success in your data science job search will require more than just knowing what the potential mistakes are. You also need to know how to best avoid them. We will look at each mistake in detail below, specifically showing you how they happen and how to avoid them.
Before we get into it, I want you to know that if you recognize yourself in any of these mistakes, you shouldn’t feel bad! I’m going over these particular mistakes because a lot of people make them, myself included! Hopefully, by learning about them, you will save a lot of time and energy by not repeating these mistakes in the future!
If you would rather get this information in a different format, I also have a video on the subject on my YouTube channel.
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. But why?
It’s inefficient. If you’re just starting out in the industry, don’t do what I did. I applied to every position I could: data scientist, data analyst, software engineer, data algorithm, and more — this is an incredible waste of time.
While you may feel like you’re accomplishing a lot, you aren’t giving yourself the best chance of finding positions you’re really suited for. For the best results, you should instead focus on applying to one type of position and become an expert at it.
Your skills won’t be as sharp. This might sound simple, but if you never focus on a single role, you’ll have a hard time garnering the required skills to land a job as a data scientist. By focusing on one type of data science role, you’ll be able to study more in-depth and quickly gain the skills that employers are looking for.
You waste time applying. You might not realize it at first, but each application will require tailoring your resume and cover letter for the job, and the time spent making those changes can quickly add up!
In an industry as specialized as data science, little may carry over from one application to the next. This is why it doesn’t make sense to waste your effort chasing interviews for positions as different as data analyst, software engineer, or machine learning engineer. The overlap in applicable skills is so slight that you are going to be wasting a lot of time on applications.
All of the above eventually leads to a low interview-to-offer rate. Data science interviews are rigorous and also highly dependent on their type of position. You will be asked different questions depending on what you are applying for. The different positions will also focus on different technical skills and knowledge.
With so much to study, if you are applying for multiple different types of positions, you’ll quickly find that the interview process becomes overwhelming. You’ll be stressed, and you won’t be able to adequately prepare and will likely flunk your interviews.
If the above reasons don’t convince you that applying for multiple types of positions is a bad idea, don’t take my word for it. Recently, a follower sent along this note that I think illustrates what I am trying to say perfectly:
I was confused if I should be looking for machine learning opportunities or general data science jobs and was applying for everything. After reading through your blog and watching videos I changed my resume focusing on the metrics and started applying for product data scientist roles. By the end of March, I had got a few calls all of which I was able to turn into onsite interviews and multiple offers. Last week, I accepted [my position as] data scientist for product analytics at Facebook. This is a dream job for me working in product data science and I wouldn’t have reached this milestone without your help.
The takeaway here is that focusing on one position type with laser precision can help your data science job search. It’s far better to hone your skills in a niche rather than trying to hit everything you possibly can. If you would like more help targeting the right position, check out this video I’ve made on the subject.
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.
Looking back at my own mindset when job searching, I can see how this might make sense. I, too, used to think this way, but I never got offers with this mindset. Eventually, I had to figure out what my interviewing problems were and learn ways to address them. You can’t rely on the sheer number of interviews to land a job.
To understand this mistake and how to avoid it better, let’s look at a few useful examples of being unprepared from my past job searches:
Unprepared for Technical Interviews
When it comes to technical interviews, practicing before the interview is essential. You may feel that you really understand a subject, but find yourself doing very poorly in an interview if you don’t take the time to prepare and practice. How does this happen? Let’s look at my experience with the coding interview as an example.
The coding interviews I’ve had typically consist of four questions: two easy, one medium, and one hard. Because I have experience with some coding-heavy data science projects in the past — doing tasks such as implementing algorithms from scratch, optimizing runtime by comparing different algorithms, and similar work — I usually considered myself a confident coder.
However, in one instance, I completely ran out of time and was only able to solve the two easy questions. I later realized that writing code in a room by yourself is very different from when someone is looking over your shoulder — scrutinizing every keystroke as you code. Furthermore, during an interview you are also expected to explain your thought process while coding, all while not introducing any bugs. When practicing for a coding interview, be sure to verbalize your thought process so that you can get a feel for the timing of an actual interview.
Not Knowing Your Interviewer
You should never walk into an interview blind. A little research beforehand can go a long way. I learned this lesson the hard way during a presentation interview.
Presentation interviews can be tricky to prepare for. Once a company asked me to present my most impactful project. I assumed my audience would be data scientists, so I introduced lots of technical terms, such as statistics and machine learning jargon, in hopes of impressing my audience with how challenging the project was.
But it turned out this was a big mistake. The audience was a mix of stakeholders, in this case data science managers and product managers. The product managers were more interested in the business impact of the project rather than the technical details. I wasn’t prepared to discuss those aspects, so naturally, I didn’t do well in that interview.
This is why it always pays to research your interviewer. A simple pre-interview email can often help reveal an interviewer’s position within a company.
As you can see from my examples above, even I have gone through interviews without adequately preparing. It was only after I shared my story and people started reaching out to me that I realized I wasn’t the only one not preparing well enough for interviews.
Some don’t expect coding questions, so they don’t prepare for it. Others have told me that they weren’t fully prepared for in-depth questions on projects or their resumes. Either way the point is that you need to do some work before you even walk into the interview.
Unfamiliar with the Product
Lastly, some’ve failed interviews because they weren’t familiar with the company’s products at all.
Some examples would be asking questions that show you obviously aren’t familiar with the company or its products. If you don’t know what Facebook’s newsfeed is or what ETA might mean for a company like Uber, you’ll look unprepared and probably won’t leave a good impression.
I really hope everyone takes interviews seriously and does their best to prepare, because people land jobs faster when they are well prepared or even over-prepared for interviews.
This is based not only on my experience going through several job searches but also on the experiences of many people who reached out to me to share their stories. On top of that, preparing for your interviews will front-load the work and reap the benefit of applying what you have learned to your day-to-day job as a data scientist.
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.
People sometimes reach out to me and ask these kinds of questions:
- Is the book you recommended in your YouTube video really helpful?
- Are the resources in your blog posts really beneficial?
- If I want to select the most useful one, which one should I choose?
I understand the motivation behind asking questions like this, but, in fact, you only know if something is helpful after you learn it. Other people’s experience might not apply to you. Keep in mind:
- Don’t spend too much time worrying about what you don’t know, instead spend time gaining new skills. For example, don’t spend hours researching which book might be most helpful, just pick one and start learning.
- Steady improvement will lead to a solid foundation for your abilities as a data scientist and lead to more successful outcomes from interviews.
So, if you realize you have a knowledge gap, and you know there’s a way to help you gain the knowledge and fill the gap, then simply take action rather than wasting time over-analyzing. Remember that you’ll never regret any investment in yourself.
All right everyone, there you have it, the three most common mistakes I see being made in people’s data science job searches. I’ve definitely learned from these mistakes in the past and I hope you take these lessons to heart so that you don’t make them as well.
DATA SCIENCE
The 3 Most Common Data Science Job Search Mistakes and Tips to Avoid Them
Do you find yourself constantly striking out during your data science job search? The truth is that you can do nearly everything right throughout the job search process, and yet one mistake could ruin your chances of landing the job.
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
Of course, success in your data science job search will require more than just knowing what the potential mistakes are. You also need to know how to best avoid them. We will look at each mistake in detail below, specifically showing you how they happen and how to avoid them.
Before we get into it, I want you to know that if you recognize yourself in any of these mistakes, you shouldn’t feel bad! I’m going over these particular mistakes because a lot of people make them, myself included! Hopefully, by learning about them, you will save a lot of time and energy by not repeating these mistakes in the future!
If you would rather get this information in a different format, I also have a video on the subject on my YouTube channel.
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. But why?
It’s inefficient. If you’re just starting out in the industry, don’t do what I did. I applied to every position I could: data scientist, data analyst, software engineer, data algorithm, and more — this is an incredible waste of time.
While you may feel like you’re accomplishing a lot, you aren’t giving yourself the best chance of finding positions you’re really suited for. For the best results, you should instead focus on applying to one type of position and become an expert at it.
Your skills won’t be as sharp. This might sound simple, but if you never focus on a single role, you’ll have a hard time garnering the required skills to land a job as a data scientist. By focusing on one type of data science role, you’ll be able to study more in-depth and quickly gain the skills that employers are looking for.
You waste time applying. You might not realize it at first, but each application will require tailoring your resume and cover letter for the job, and the time spent making those changes can quickly add up!
In an industry as specialized as data science, little may carry over from one application to the next. This is why it doesn’t make sense to waste your effort chasing interviews for positions as different as data analyst, software engineer, or machine learning engineer. The overlap in applicable skills is so slight that you are going to be wasting a lot of time on applications.
All of the above eventually leads to a low interview-to-offer rate. Data science interviews are rigorous and also highly dependent on their type of position. You will be asked different questions depending on what you are applying for. The different positions will also focus on different technical skills and knowledge.
With so much to study, if you are applying for multiple different types of positions, you’ll quickly find that the interview process becomes overwhelming. You’ll be stressed, and you won’t be able to adequately prepare and will likely flunk your interviews.
If the above reasons don’t convince you that applying for multiple types of positions is a bad idea, don’t take my word for it. Recently, a follower sent along this note that I think illustrates what I am trying to say perfectly:
I was confused if I should be looking for machine learning opportunities or general data science jobs and was applying for everything. After reading through your blog and watching videos I changed my resume focusing on the metrics and started applying for product data scientist roles. By the end of March, I had got a few calls all of which I was able to turn into onsite interviews and multiple offers. Last week, I accepted [my position as] data scientist for product analytics at Facebook. This is a dream job for me working in product data science and I wouldn’t have reached this milestone without your help.
The takeaway here is that focusing on one position type with laser precision can help your data science job search. It’s far better to hone your skills in a niche rather than trying to hit everything you possibly can. If you would like more help targeting the right position, check out this video I’ve made on the subject.
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.
Looking back at my own mindset when job searching, I can see how this might make sense. I, too, used to think this way, but I never got offers with this mindset. Eventually, I had to figure out what my interviewing problems were and learn ways to address them. You can’t rely on the sheer number of interviews to land a job.
To understand this mistake and how to avoid it better, let’s look at a few useful examples of being unprepared from my past job searches:
Unprepared for Technical Interviews
When it comes to technical interviews, practicing before the interview is essential. You may feel that you really understand a subject, but find yourself doing very poorly in an interview if you don’t take the time to prepare and practice. How does this happen? Let’s look at my experience with the coding interview as an example.
The coding interviews I’ve had typically consist of four questions: two easy, one medium, and one hard. Because I have experience with some coding-heavy data science projects in the past — doing tasks such as implementing algorithms from scratch, optimizing runtime by comparing different algorithms, and similar work — I usually considered myself a confident coder.
However, in one instance, I completely ran out of time and was only able to solve the two easy questions. I later realized that writing code in a room by yourself is very different from when someone is looking over your shoulder — scrutinizing every keystroke as you code. Furthermore, during an interview you are also expected to explain your thought process while coding, all while not introducing any bugs. When practicing for a coding interview, be sure to verbalize your thought process so that you can get a feel for the timing of an actual interview.
Not Knowing Your Interviewer
You should never walk into an interview blind. A little research beforehand can go a long way. I learned this lesson the hard way during a presentation interview.
Presentation interviews can be tricky to prepare for. Once a company asked me to present my most impactful project. I assumed my audience would be data scientists, so I introduced lots of technical terms, such as statistics and machine learning jargon, in hopes of impressing my audience with how challenging the project was.
But it turned out this was a big mistake. The audience was a mix of stakeholders, in this case data science managers and product managers. The product managers were more interested in the business impact of the project rather than the technical details. I wasn’t prepared to discuss those aspects, so naturally, I didn’t do well in that interview.
This is why it always pays to research your interviewer. A simple pre-interview email can often help reveal an interviewer’s position within a company.
As you can see from my examples above, even I have gone through interviews without adequately preparing. It was only after I shared my story and people started reaching out to me that I realized I wasn’t the only one not preparing well enough for interviews.
Some don’t expect coding questions, so they don’t prepare for it. Others have told me that they weren’t fully prepared for in-depth questions on projects or their resumes. Either way the point is that you need to do some work before you even walk into the interview.
Unfamiliar with the Product
Lastly, some’ve failed interviews because they weren’t familiar with the company’s products at all.
Some examples would be asking questions that show you obviously aren’t familiar with the company or its products. If you don’t know what Facebook’s newsfeed is or what ETA might mean for a company like Uber, you’ll look unprepared and probably won’t leave a good impression.
I really hope everyone takes interviews seriously and does their best to prepare, because people land jobs faster when they are well prepared or even over-prepared for interviews.
This is based not only on my experience going through several job searches but also on the experiences of many people who reached out to me to share their stories. On top of that, preparing for your interviews will front-load the work and reap the benefit of applying what you have learned to your day-to-day job as a data scientist.
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.
People sometimes reach out to me and ask these kinds of questions:
- Is the book you recommended in your YouTube video really helpful?
- Are the resources in your blog posts really beneficial?
- If I want to select the most useful one, which one should I choose?
I understand the motivation behind asking questions like this, but, in fact, you only know if something is helpful after you learn it. Other people’s experience might not apply to you. Keep in mind:
- Don’t spend too much time worrying about what you don’t know, instead spend time gaining new skills. For example, don’t spend hours researching which book might be most helpful, just pick one and start learning.
- Steady improvement will lead to a solid foundation for your abilities as a data scientist and lead to more successful outcomes from interviews.
So, if you realize you have a knowledge gap, and you know there’s a way to help you gain the knowledge and fill the gap, then simply take action rather than wasting time over-analyzing. Remember that you’ll never regret any investment in yourself.
All right everyone, there you have it, the three most common mistakes I see being made in people’s data science job searches. I’ve definitely learned from these mistakes in the past and I hope you take these lessons to heart so that you don’t make them as well.
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