What I Learned After 30+ Data Scientist Interviews

Hello everyone! Today, I’m sharing my experience and the main lessons I learned after attending more than 30 data scientist interviews. It’s been a tough journey, but it has taught me a lot about the industry, the interview process, and what it really takes to succeed.

A Bit About Me

For those who are new here, my name is Nandi Vardhan Reddy, and I have been working as a data scientist for more than two years. After switching companies, I thought getting a new job offer would be easy. But to my surprise, it took me about 60-65 days to get my first offer. During these months, I made many mistakes, learned a lot, and gained valuable insights that I want to share with you today.

1. Communication Is Key

While communication is important for every role, it is especially crucial for a data scientist. Why? Because as a data scientist, you're often building models that are complex and sometimes work like black boxes. Your job doesn't stop at making these models—you also need to explain them to both technical and non-technical people.In my interviews, I found that many companies cared less about my coding skills and more about how well I could explain the decisions I made while building a model and the trade-offs involved.It's important to practice explaining complex ideas in simple words. This can make a big difference, especially when you're talking to people who might not have the same technical background as you.

2. Domain Knowledge Matters

Another important lesson I learned was the value of domain knowledge. In interviews, especially for data science roles, many companies expect you to understand the business domain you're working in. In some interviews, I was asked to solve real-world business problems, and I realized that without proper domain knowledge, it's very easy to get stuck.For example, I had an interview with Wayfair, an e-commerce company specializing in home décor. One of the rounds involved solving business problems related to ad bidding prices, and because I didn't have much domain knowledge in this area, I struggled. I quickly realized that understanding the business and its challenges is just as important as technical skills.Whether you're applying to a healthcare company like Tata 1MG or an e-commerce giant like Wayfair, understanding the domain can make or break your performance in the interview.

3. Don’t Overcomplicate Your Solutions

One of the biggest mistakes I made early on was making my solutions too complicated. I thought impressing interviewers meant showing off complex, advanced models. But I soon realized that simple models are often more effective and efficient.For example, when working on sentiment analysis, many people would immediately go for Large Language Models (LLMs). But often, a simple Naive Bayes model or a basic approach using BERT can give better results, especially when considering cost efficiency and ease of understanding.Most interviewers like candidates who can begin with simple, basic models and explain the reasons for choosing to move to a more complex model.

4. System Design Is Becoming Crucial

I didn't expect to learn how important system design is for data scientists. While it's usually linked with software engineering, many companies now include a system design round in their data science interviews.In these rounds, you're expected to design systems for real-world applications like recommendation engines, model deployment pipelines, and end-to-end orchestration. You need to think beyond just building a model — you have to consider how it will scale, be deployed, and maintained.From what I've seen, system design questions for data scientists usually focus on things like creating recommendation systems or improving machine learning workflows. So, it's important to be ready for these kinds of questions too.

5. Soft Skills and Cultural Fit Matter

While technical skills are important, I also realized how important soft skills and cultural fit are. Many companies want candidates who match their values and can blend well with the team. This was especially clear during the behavioral rounds of my interviews, where hiring managers focused on my ability to work together with others and fit into the company culture.It's not just about finishing the work — companies want to hire people who will get along with their team and add positively to their culture.

6. Resilience Is Key

Lastly, the most important lesson I've learned in this journey is resilience. Job hunting is tough, especially when you face rejection again and again. There were several times when I thought I would get the offer, but then I was disappointed. Sometimes, I felt frustrated, but I kept reminding myself that each interview was a chance to learn, and eventually, things would work out.I also understood that rejection is not the end; it's just a part of the journey. With each rejection, I learned something new, whether it was a technical concept, a way to improve my communication, or a better way to prepare for future interviews. Resilience kept me going, and finally, I got the offer I wanted.

Final Thoughts

To sum it up, my journey through over 30 interviews has been long and tough, but also very rewarding. I've learned a lot about what it takes to get a data science job, and I want to share those lessons with you. If you're looking for a job right now, I hope my experience helps you avoid some common mistakes and makes your journey easier.Remember, the path to success is never straight. It's full of ups and downs, but with the right mindset, persistence, and focus on learning, you'll eventually reach your goal.

Thanks for reading! Good luck with your job search, and stay strong!