Before applying at Lyne I was mainly focusing on heightening my learning curve in the field of Machine learning and Neural networks, working on personal projects as well as semester projects. I knew I had to apply as soon as I read that it’s an opportunity to see what a career in Machine learning and Data Science, has always intrigued me.
The first round was resume shortlisting, I was then qualified for the first technical round in which I was given a few business problems and asked to come up with a technical solution that could solve them and a few technical questions regarding data normalization and machine learning algorithms, for instance, given a dataset having an imbalance between the different classes what are the techniques you would adapt to tackle it. The feedback after this round was very positive, so I had my hopes high.
The next round was an HR round where one of the Co-founders of Lyne was also present to help me understand the business objectives of all the work that happens at lyne. A few days after I was provided with the offer letter and assigned a reporting manager along with the list of tasks.
When I started in May, I felt so unprepared and worried — everyone I spoke to was telling me how different work would be from my coursework. As a person that prides herself on preparing for everything, the idea of adapting quickly really freaked me out, but I realized soon that what these people were saying is true! Much of your job is truly based on what you learn while working.
As students, we’ve all asked ourselves the age-old question: “When am I ever going to need this in the real world?”, most often during an exam cram session or a particularly difficult homework problem. This summer, I’ve found that we do need those things in the real world — just in a different capacity.
The first month was somewhat like a probation period where I had to go through a lot of documentation and already developed codes from the other colleagues working there and understand them and try to think of ways to optimize those. Next, I was assigned a task to normalize and clean a huge dataset containing data of people, during this project I was introduced to various new technologies such as AWS Glue and other AWS services designed to cater to ETL tasks. Then my next project was to develop a script that can be used to predict the work email of a person if he joins a particular company by using the syntax data generated. I am currently working on creating a custom trained to classify whether a company produces a podcast or not.
My internship experience was out of this world, and I am so happy I was able to get a taste of what the “real world” is going to be like in two years’ time. I made so many amazing friends, completed challenging and fulfilling work, and got to do what I love every single day. If you’re an engineer thinking about doing an internship or co-op, take the opportunity. You won’t regret it, I promise!