From Capital One to New Heights: Jayandra Lade’s Data Science Journey

Jayandra Lade’s journey into data science mirrors the path of many professionals who began in computer science and gradually shifted toward the world of data analytics. After earning his bachelor’s in computer science from GITAM Deemed University, Jayandra recognized the growing demand for data-driven insights. He pursued a master’s in data modeling and database administration from George Mason University.

His career kicked off at Capital One, where he gained invaluable experience in data migration and marketing automation. As his knowledge and skills grew, so did his ambition—leading him to land a coveted position at Amazon. Along the way, Jayandra mastered the art of interview preparation, continuously adapting to industry trends. In this article, he shares the key lessons and strategies that helped shape his career in data science.

I first started out at GITAM Deemed University, where I got my bachelor’s degree in computer science, and that’s where I got introduced to data science and data analytics.

I then slowly started switching over to the data analytics side; however, at the time, 2015–2016, there weren’t a lot of universities that offered programs specific to data analytics. Most universities focused on computer science and only a couple of courses on data analytics. That is why I picked George Mason University, where I got my master’s degree in data modeling and database administration.

I first started out as a data analyst contractor at Capital One. It wasn’t really a full-time role, but I got a lot of good experience since I shuffled between three different teams in three years.

First, I got to work on migrating data from Teradata to Snowflake. That’s when Snowflake started gaining popularity (2018), so luckily, I got ahead of the curve by acquiring a lot of knowledge on Snowflake through these projects.

Here, I was basically the sequel person to validate the tables. However, the best thing that happened to me at Capital One was when I got into the marketing team at the end. I was working on real-time marketing for users, like triggering email campaigns and phone notifications, doing AB testing, building table dashboards, and so on.

So, this was the keystone of my work experience and where I exposed myself to the industry and figured out how it works. This is how I improved a lot of my abilities in SQL Tableau and had some exposure to AWS, S3 buckets, EC2 instances, etc.

I actually had a friend who worked at Capital One who referred me to a consultancy company. That company then got me an interview with Capital One.

However, I was interviewed for the contract role. It wasn’t a regular Capital One role, so the interview was short and sweet, with a good amount of technical questions. It wasn’t like a long day of interviews.

I don’t remember specifically, but I used everything I could. At the time, I went through many interviews, which provided me with insight into what the industries expect, like answers to: What kind of SQL questions do they ask? What kind of Python questions do they ask? What other types of technical or non-technical questions might they ask?

I also remember using Interview Query for Capital One to practice some SQL questions, Python questions, etc.

I also remember using Interview Query when I was joining SIMON Markets, and since then, I’ve noticed just how much the platform has changed and how many more options it has.

I think people in this industry need to focus on two things. First, familiarity with new tools and cutting-edge technology. Of course, companies don’t expect you to know everything, but they want to see that you’ve heard about this new stuff.

The second thing is to practice. For example, the Interview Query questions helped me a lot, especially A/B testing questions because they are a bit more specific and they aren’t very common.

Alongside that, I also appreciated how many of these questions or solutions had comments from the community. People comment on the solutions or offer different types of solutions.

This helps you understand different people’s opinions and methodologies you can use in the real world and not just the right answer. I think that’s one of the major hits for Interview Query.

What else helped you with preparing for interviews?

I was recently interviewed for Amazon, and I often used your community forums. I also tried out the interview guides, which helped me understand what kind of questions Amazon usually asks.

Well, I successfully landed a job at Amazon—my interviews cleared. I completed all of the technical rounds, the behavior rounds, and the rest of the interview.

The first interview round lasted about a month and was completely technical. Amazon was specifically interested in one kind of SQL question, like using complicated functionalities, but Interview Query helped me practice these questions, so I was prepared for that round.

After one month, I scheduled my power day, four or five interviews in one day that can last up to 5 hours. In these interviews, I mostly talked about myself and AB testing statistics and less about SQL.

A/B testing was what I was most stressed about, so I was heavily dependent on Interview Query to find all the topics about how Amazon thinks because reading a book is very different from what they apply in the industry.

I think these kinds of situations come to everyone. I always kept an eye open for any new opportunities, and several months ago I started to actively look for a change. Then, a friend referred me to Amazon, and that’s when the process started. Then I cleared the interview, and now I’m in the team matching phase!

I started in July and finished the technical round at the end of July.

Jayandra Lade’s journey into data science is a testament to the importance of adaptability, continuous learning, and preparation. From building a foundation in computer science to acquiring specialized skills in data analytics, his career trajectory showcases the value of hands-on experience, mastering industry-relevant tools, and honing interview techniques. By leveraging platforms like Interview Query and staying ahead of technological trends, Jayandra successfully navigated his way to a role at Amazon.

His story is a reminder to aspiring data professionals that success lies not only in technical expertise but also in persistence, resourcefulness, and a willingness to grow with the industry. Whether you’re just starting out or seeking your next big opportunity, Jayandra’s advice offers a valuable roadmap for making your mark in the data science world.