Arnab Borah graduated from Tezpur University and went to pursue MS from University of California, Santa Cruz where he specialized in NLP and ML. During the course of his Bachelor's and Master's, he interned at Verisign and Capsilon. He is currently working as a software engineer in eBay and is also a member of its Board of Advisors. His primary domain is Ads and Search Monetization and Search Ranking.
Have you ever wondered what it's like to work in Silicon Valley but had no one to seek guidance from?
Our second webinar with Arnab Borah addressed this question in the most elaborate way possible. It gave us an insight into the ever-expanding world of ML and helped us broaden our perspectives.
Contrary to the popular belief, Mr. Arnab mentioned that it's not necessary to know the language your company works in. One can definitely have favorites but he/she should be comfortable with whatever they're asked to work with. After all, it's a continuous learning process. He started out with Python which is also his personal favorite. eBay specifically uses R, Scala, Hadoop, Spark for the data engineering section and its own modified version of C for software deployment.
When asked to present his thoughts on this topic, Mr. Borah said it's an ever evolving field. Multinational companies have begun to set up bigger offices in India and have started collaborating with colleges to open AI Labs, thereby opening new areas of research and development. This broadens the scope, especially for students and young graduates.
It can be quite overwhelming to choose one out of so many different fields in CS especially for a newbie. Our speaker advises us to focus more on CS and basic programming skills in the first semester. In your first year you can put an emphasis on your software engineering skills. It's always better to start early. There are plenty of resources online to learn from. You can discover and implement a wide range of topics and then pick your choice.
If you're opting for higher studies or research, it's best to look for a way to expand your knowledge and implement the algorithm in a new way. Universities generally look for interest and capability in the candidates. They focus on what you can do given the resources rather than what you have already done. If you're going for a career in the industry then simply focus on being a good software engineer and include some machine learning projects. Again, if you're not sure which way to go, you can always mold the same projects in two ways - one for Research and one for the Industry. It's best to have two resumes that highlight the different skills you need for the respective fields.
No matter how big or small the project is, it is encouraged to put it up on github for open-source as developers across the world would be able to collaborate. Mr. Borah took this opportunity to release an open-source project that he and his team has been working on - FLING : Fast Linguistics which has libraries used for unsupervised linguistic tasks. Not only this, there are tonnes of open-source projects related to Machine Learning which one can contribute to and make it a part of their resume. We also announced the opening of an official TU CSE Department github page dedicated for open-source contributions.
Arnab Borah was the first to bag a job without any prior work experience in Santa Cruz and is also a part of the hiring team for data scientists in eBay.
He emphasized on maintaining a good profile.
1.
CGPA is something all recruiters look for but it's not mandatory. If you don't have a great CGPA, compensate with great internships and projects.
2. GitHub account is a must for computer science students.
3. You should have at least 4 to 5 projects on your resume. But do not just focus on one aspect of ML. Instead cover as many you can.
4. Students often ignore how important basic s/w skills are. Solve interview questions on various sites- ACM-ICPC, CodeJam etc. By the end of it, you should be a good coder.
5. Do not brag on your resume. Mention only those skills that you're really comfortable with. Make a separate section for Machine Learning skills.
6. Lastly, it's not compulsory but it's highly appreciated if you could contribute something to open source.
Referrals work only once in the industry. It might prove to be of some help for students to enter the industry but after that , it's your skills that would define you. So Mr. Borah suggested to work more on your skills and upgrade your resume so that you make sure recruiters notice you. Even after getting a job, it's possible to get offers from other companies if you develop some highly specific skills which the companies are looking for.
It’s possible to pursue Machine Learning and yet engage in other relevant fields of interest such as cyber-security. Nowadays, finance companies hire Machine Learning engineers to secure and optimize their investing platforms. Mr. Borah mentioned that Cisco hires ML engineers to optimize their network security platform. It's all about finding networking related problems which need data optimizing solutions. It’s less likely to find such jobs in India but there are companies abroad who look out for such candidates.
It is not always possible to be able to achieve one’s set goals straight after graduation. It can be quite a topsy-turvy journey and unpredictable as well. There might be situations when one would have to take extra certifications for a particular job profile or work in startups for job experience. The key is to chalk out all possible paths to your goal and then figure out which one works best for you in bridging the gap between you and your career. It is also encouraged to look up to people who have achieved their goals in a similar fashion and learn from them.
Mr. Borah also recalled the fallbacks in his journey - how he was confused between pursuing MS or MBA. It was then when he started exploring Machine Learning, found his passion and then made a concrete decision to go abroad for MS.
In the end, everything boils down to practice, patience and determination!