How I secured an internship at NVIDIA as a Data Scientist
NVIDIA is a company that manufactures Graphical Processing Units (GPUs) for gamers and for deep learning and AI purposes. It has recently ventured across AI, and it is building computing platforms for self-driving cars, medical applications and recommender systems. It is currently the 8th most valuable company in the world and its stock and revenue are growing at a steady pace.
Getting an internship at NVIDIA can be tough, especially to become a data scientist intern during the graduation days. One must put significant effort in the process and also demonstrate skills that are most needed in the industry. One of the best ways to ensure that employers and recruiters recognize your talent is to build interesting portfolios of your work and showcase your skills and strengths. There are other ways to get into NVIDIA either through going to a great college and ensuring that one is maintaining a good GPA.
Below are some of the steps that I’ve taken to get an internship at NVIDIA as a data scientist.
- Practice consistently
This is the most important tip that I put a lot of emphasis and that was how I was able to build a solid portfolio of my projects along with learning constantly the latest skills and tools needed for machine learning. When someone works consistently, it often is the case that one need not put a lot of effort as compared to doing things all at once without taking gaps. Therefore, one of the things that I would suggest to do is to consistently work on data science in ways such as reading blogs, articles, listening to videos, building interesting projects along with many other things as well. Ensure that you do all of these activities regularly so that with effort, you learn to be making progress in the field and build a good understanding of the topics and advancements in the field of deep learning and data science.
2. Develop the habit of reading other’s code
While it is good to practice programming and learning to individually solve some complicated tasks with the right programming skill, it should be taken into account that reading someone’s code would help you also to be a better coder and develop the right skills needed for a data scientist internship. After learning to code and developing the habit of going through other’s code daily, there would be a significant improvement in your coding skills as well as you would be able to learn and apply your knowledge in many areas. Therefore, it would be wise to learn how to code and also read other’s code as this leads to you becoming a better programmer.
3. Build a strong portfolio of your work
This is one of the most important steps to secure internships or full-time jobs in a lot of these top-tech companies. There is a lot of competition to get into a data science position. People who are pursuing masters in various fields such as statistics, data mining, data analytics, data science and computer science compete for these roles. To make things worse, these positions often require some good amount of experience in machine learning and data science. Thus, work must be done that separates you from the competition which could be with the help of solid portfolios demonstrating your work and experience in the field. During the interviews, rather than just saying your experience it is always a good idea to showcase your work and explain it to them so that they also know that you have done a great deal of work and they would be willing to offer you the job.
Feel free to take a look at my portfolio where I highlight all of my skills in the field of machine learning and data science. Below is the link.
suhasmaddali (Suhas Maddali ) (github.com)
You can also follow me on GitHub so that you get to know the latest changes and commits that are available that I update regularly in my account.
4. Go through online courses
While it is intuitive that going through online courses can increase the chances of getting an internship, there are only a handful of people who constantly go over the course and revise it to an extent that they can explain the same information during the interviews. Hence, I suggest that you could revisit those courses that you have already gone through in the field of machine learning and data science. Later, you can revisit and revise the topics so that you have a solid idea of the topics which you could in turn use to build a good portfolio as well.
5. Learn all the important libraries for ML and artificial intelligence
Assuming that you are going to be using Python as your language for machine learning, I would suggest to get yourself familiar with these libraries as they are used in a large number of projects. Some of the libraries include Seaborn, Pandas, Matplotlib, Numpy and many others. Familiarizing yourself with these libraries can ensure that they could be used for various deep learning applications. As a result, one would be able to perform data visualization along with data processing and modelling with ease with the use of the right libraries and tools from ML.
6. Having a strong theoretical foundations in ML and data science
With a large number of libraries and tools available on the internet for the purpose of data science, it is easy to think that just learning to use these libraries would be enough to become a good machine learning engineer. However, it is important to note that having a strong theoretical foundation ensures that though there is a change in the libraries or methodologies used for machine learning, one can also learn to implement these libraries without the need to actually learn everything from scratch. This ensures that even when there is upgrade in libraries or deprecation of older libraries, one who has a good theory knowledge can get to implement them quite easily without much effort.
7. Begin to question things and look for alternate approaches
When you being to read articles or blogs that convey ML concepts, it would be a great habit if you question why a particular approach is working while others are failing. In this way, you would be able to account for the facts that make the things to work in ML and also learn the other approaches and their disadvantages for the problem at hand. Therefore, you tend to improve on your analytic approach and begin to get better at the craft as well.
Conclusion
All in all, these are some of the important tips that I would highlight that have helped me to get an internship at a prestigious and highly valuable company (NVIDIA) as a data scientist intern. These are some steps that I would be suggesting anyone who is looking forward for the best opportunities to work in some of the top-tech companies in the field of machine learning and data science.
If you are looking to build an interesting portfolio of your work and are looking for the best ML projects to include in it, feel free to take a look at my other article where I have highlighted various ideas that you might consider when starting a portfolio either by building your website or using GitHub. Below is the link to the article. Thanks!
Best Projects to Showcase in Machine Learning Portfolio | by Suhas Maddali | MLearning.ai | Medium
If you want further information about my work, below are the details where we can connect and you could also view my work.
GitHub: https://github.com/suhasmaddali
LinkedIn: https://www.linkedin.com/in/suhas-maddali/
Facebook: https://www.facebook.com/suhas.maddali
