How I Got 4 Data Science Offers and Doubled my Income 2 Months after Being Laid Off

about me Feb 08, 2023

In late 2018 I was working for a startup, and my future looked bright. I had just received a great performance review and a 33% increase in my salary. I was motivated and ready to take on the next challenge.

And then I got laid off.

That was not the challenge I was expecting. I quickly felt overwhelmed and full of anxiety. I didn’t yet have much experience, and when I looked at data science job openings I soon realized that there was much I didn’t know. How was I going to find a new job? How was I even going to get interviews?

What followed was a difficult and stressful time. I had to figure a lot out on my own, and it led me to a conviction - No one should have to navigate finding a data science job alone. That’s why I’m sharing my journey here. I hope that in reading about what I did and what I learned, others can navigate their job search with success and less stress.

Getting Ready

To start I decided to lessen my task somewhat I decided to narrow my focus. Most data science positions on the market were product-driven so I decided to focus my preparation on that, and it is that preparation for product-driven positions that I will be outlining in the rest of this blog.

It still wasn’t easy. Out of about 50 raw applications, I only got 3 interviews. I did learn that referrals were much better: out of 18 referrals, I got 7 interviews.

Because getting interviews was tough I knew that I couldn’t waste a single opportunity. I had to be prepared. There were 6 sections of content that I studied to prepare for my interviews. Here are the approaches and resources I used.

Product Sense

I didn’t have a lot of product knowledge to start with so I focused on the classic read and summarize method with the following resources to expand my knowledge. I then developed structures for answering any product sense question and practiced my answers.



Practice makes perfect! I grinded SQL questions until I saw a drastic improvement in my skills


Statistics and Probability

For this topic, I mostly did review. There is a lot of content, but for product data science interviews the questions were never very difficult, so I didn’t dive too deep into advanced topics.


Machine Learning

Interview questions for a product data scientist are mainly geared toward how to apply machine learning models rather than the underlying math and theories. However, I still used the following resources to bump my skills up for interviews.



To present the project well you want to make it interesting and challenging. To do this, I made sure to think through the details of whatever project I wanted to discuss. Here are some questions I used to do that:

  • What were the goal and the success metric of the project?
  • How do you decide to launch the project?
  • How do you know whether customers are benefiting from this project? By how much?
  • How do you test it out? How to design your A/B test?
  • What was the biggest challenge?
  • What was an interesting finding?

Just thinking about these things wasn’t enough though. I also practiced, and I practiced out loud. I even practiced presenting to my family to ensure my grasp of the material and ease of communication. If you can engage the people you know, an interviewer, who is required to listen, doesn’t stand a chance.

Behavioral Questions

In preparing for technical interviews, it can be easy to forget about the behavioral interview, but this interview is also essential for landing a job. The type of questions I faced and behavioral interviews can be split into 3 categories:

  • Why us?
  • Introduce yourself.
  • The biggest success/failure/challenge in your career.

To be prepared for these interviews I made sure that I understood the company’s mission and values and that I had a few stories about past work experiences that I could discuss. Just like with the other interview types practice was essential for making sure that my answers worked.

In Interviews

Preparation and practice were super important, but I also had to make a good impression in the interviews. I followed 5 basic rules to ensure that I was always presenting myself well in on-site interviews.

  1. Always clarify questions before answering.
  2. Organize the answer for all questions.
  3. Don’t panic when you don’t know the answer.
  4. Attitude matters.
  5. Research the company.

Those rules and my preparation really paid off! I was able to achieve a 100% on-site interview to offer conversion rate.


Once I landed offers I wasn’t quite done. Another rule I followed on my job search journey was to always negotiate.

Haseeb Qureshi has a very helpful guide on negotiating a job offer (with scripts!) which I followed religiously during my offer negotiation phase. Negotiating really paid off. I was able to get at least a 15% increase to all of my offers and a 25% increase to the highest. So, I say with confidence that you should always negotiate!


What was the result of all of this?

After losing 11 pounds and lots of cries and screaming (job hunting is stressful and it is okay to admit that), I finally got 4 offers within 2 months of being laid off. 3 of those offers were from companies that I have never dreamed of joining: Twitter, Lyft, and Airbnb, where I ultimately joined.

I would never have been able to do this without the support of my family and friends. I often feel overwhelmed, and it didn’t take me long into the journey to discover that getting a job can be a job in itself.

I hope that this blog has taken even some of the stress off any data scientists out there in similar situations. If you want to know more about what resources and advice I offer for job searching, be sure to check out my videos and other blogs.

Do you want to read an even more detailed version of this blog? Check out the longer version here.

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