How to bag an Industrial Research Internship in India

What is an industrial research?

Research and Development is an essential part of most corporates in today's fast-growing environment and ever-developing science and technologies. Industrial Research refers to joining any industry to innovate in their R&D labs or department. Although most giants have their research units in their headquarter based regions (Eg: US), many of them offer opportunities in India. One criterion for R & D in any industry is that the end product or innovation should benefit the industry and its customer base.

Industrial Research Vs. Academic Research

While there are many similarities between the impact of academic and industrial research, both involving serious and diligent research, ending in publications or patents, there are some subtle differences that one should consider.

1. Funding

The funding in an academic research forum depends on the professor and the university / research institute’s rules and regulations. The collaborations and reach of the professor might be a factor that contributes to this. The researchers may need to apply for grant applications for equipment/travel etc.

In industrial research, you are a regular employee of the company. Hence, according to the company's payment structure, you will be provided with a regular monthly salary. Industrial researchers usually do not need to apply for external funding from grant agencies to cover payroll, travel, or equipment, although, at some institutions, they are allowed to do so.

2. Your choice of research?

In academia, you can always look for a professor to work under depending on your interests. Hence it is likely to be able to research in a field that you aspire to work on. It might also help you specialize in the area you've always worked on. You don't necessarily have to step outside of your comfort zone.

In Industrial research, you join as an individual contributing to the core R&D of the company. The company generally prefers working with the technology and topics which they cater to. Hence you might have to just work in a field suitable to the company's ideas even though they might not fit well in your spectrum.

However, most corporate research laboratories state the criteria they will develop their work in from before. Hence as an applicant, you can choose to apply in labs that are converging with your interests.

3. Open-sourcing and free access.

Academia generally encourages open access, unless they are bounded by any reason not to. Hence most of the code and work can be made available to the public. Open source code always gains more responses from other researchers and probably gives more scope of ideation if collaborated with other researchers. On an ethical norm, I feel it provides a feeling of accomplishment for others to be freely able to use the material you've created.

Strict non-disclosure guidelines generally bound industrial research. Sometimes, they may be allowed to open source the code base and datasets they work on. However, usually the experimentation may be done on the company's data and hence requires to be highly confidential. The R&D may be used to enhance company products, and although the papers of innovation may be published, the code is generally kept on the company servers itself.

Therefore, as an individual, you might not have the freedom to open source your work for the public. However, it is a proud feeling to see your research embedded into a company product or work and used in a real-time environment.

Requirement

Most industrial research platforms require the candidate to have extensive experience in Data Science and AI. Hence, a Masters/Ph.D. candidate is always preferred.

Very few corporates take a fresher or undergraduate in their research team. Most expect the candidate to have already some well-published works and projects that fit the industry's topics. But worry not! Some big giants take capable undergraduate candidates as well.

What would I need to apply?

CV: It is a necessity to have a well built and precise CV. Whenever you are working on your CV, think from the recruiter's perspective and what they make like to see. Have compelling content that shows you're a match for the respective job.

Letter of Recommendations: Some positions require a set of letters of recommendation. It is generally required from your guides, professors and peers who you have worked with. Typically, companies prefer guides that you've worked with or who have mentored you previously. Hence, keep good relations with your professors and work with a few in college to make sure you have professors who you can ask for help from, whenever required

Required Questions: Some companies tend to give questions that may be used for shortlisting. These questions may ask you about your problem-solving skills, how would you tackle certain situations, motivations, and aspirations, why are you a precise fit for their program, etc. Try not to give the generic answers and rely on your thought process for answering these. Be innovative and try to think out of the box.

Research Labs in India which take freshers or undergraduates

MSR: https://www.microsoft.com/en-us/research/lab/microsoft-research-india/

IBM Research: https://www.research.ibm.com/labs/india/

Adobe Research: https://research.adobe.com

Samsung R&D: https://research.samsung.com/sri-b

Google AI Labs : https://ai.google/research/

Key points to keep in mind

1. Build your CV well

CV acts as a first impression to any recruiter, and first impressions make a huge difference. Shortlisting of candidates is done on the basis of the CV. Each job has a specific requirement. An applicant applying for an industrial research position will have a very different CV from other applicants.

Take time to understand what skills benefit your applications and enhance those skills. There are plenty of courses in Coursera which can earn your certification of these skills. (Data Science, AI, Deep Learning, etc.).

Self-done projects also enhance your CV multifold. Kaggle is a great place to start with for data science-based aspirants. Always be thorough of the job description and understand the needs of the job.

If you feel like you are not well versed with the topic of their requirement, try to find a suitable course or a problem statement on Kaggle concerning it and enhance your skills. Keep your CV neat and crisp, not more than two pages.

2. Stay active on LinkedIn and career sites (referrals)

In a world of digitalization, it is extremely important to keep a social identity. With job opportunities and openings well available online, your profile should always be up -to -date to apply. LinkedIn is a great platform to showcase your skills and profile for job applications online. You can also follow your favorite organizations and connect with their employees to know more about the job in detail.

It is good to make a reliable network and plenty of connections. Maybe you can request a referral too! It is also necessary to keep tabs on the career sites of the companies you want to apply to.

There often positions updated, which one can easily apply to by generally providing the CV and a cover letter. It is excellent to make an account on these companies' career pages and get direct notification of your mail.

3. Reach out to the person of interest with your idea and how you can collaborate.

It is an intuitive idea to find common ground between your skills and what is required in your dream internship. Always be through with the research which they are doing and stay up to date with their work.

Find out ways in which you can personally collaborate with their current job, which will benefit them. Mail the researchers with your idea and how it may help their current research. Show them your interest and requirement. If your idea strikes them as interesting, they may get back to you and follow up with you as well!

4. When you see an opportunity, Apply!

It is a good idea to keep applying. Wherever you see an opportunity that matches your interest, go ahead and apply for it. Don't will power if there is no answers or rejections. The industry is very competitive, and you have to keep trying to make your mark.

PROCESS GENERALLY TAKEN UP AFTER SHORTLISTING

1. General coding round:

Since it is an industry-based job, many companies prefer their candidates to have experience in coding with data structures and algorithms. The company could have an online coding round for shortlisting or maybe an interview with an industry expert according to their guidelines.

Sometimes it may be skipped since you've applied for a research-based position. However, it is good to expect a coding round for selection and be prepared likewise. I had applied for Adobe's MDSR (Media and Data Science Research) team. There was a data structure coding round followed by an interview on DS and Algorithms and essential computer science topics including DBMS, Operating Systems, etc.

2. Research Interviews:

• CV based projects All the projects that you have completed should be adequately revised. It is very likely for the interviewers to question the specifics of the projects. The requirement and thought process might also be examined. The skills might be picked up from what you've used in the projects and might be asked in detail.
• Basics of important concepts It is imperative to keep your basics clear. If you are giving an interview for AI and ML field, the necessity to know the very basics such as cost function, gradient descent, and backpropagation is undeniable. Also, knowledge of probability, statistics, linear algebra is of very high importance. Hence spend a good time revising your basics and clear any concepts which you are unsure of.
• Intuitiveness and thinking skills The interviewers, especially in the research field, want someone who can think out of the box. Coming up with new ideas and expressing yourself during the interview might help them gain insight into your cognitive ability.

3. Qualifying task:

There might be a situation where the recruiting parties would want the candidates to complete tasks. This can be a problem statement related to their position. E.g., It could be a classification task of images if you're applying for a firm that researches in Computer Vision.

This is done for the evaluation of your actual skills, your technique, code efficiency, and intuitiveness. You can be later asked to explain your approach and code.

Always Remember to

• Be Confident: A critical skill to bag your dream internship is always to have confidence in yourself. Never think of yourself to be lesser than anyone. Be confident in interviews and let the interviewer know if you don't remember a concept or can't understand the question. Don't beat around the bush. Generally, interviewers try to understand the candidate.
• Be Innovative and Intuitive: Be alert while answering questions, whether online or in an interview. In research, juts brains are not valuable; instead, how innovatively you can think of a problem is also essential. Be open-minded and explorative. Try to think of the same problem with different perspectives and come up with new ideas.
• Think out loud It is critical to express yourself. Hesitation in interviews is common. Many times you may feel that whatever you're thinking isn't right and the interviewer should not know. However, many interviewers guide you if you figure out loud. If you tell them what you're thinking, they may help you out and correct you. You never know what may turn out to be in your favour.
• Learn from your mistakes It is common to have rejections and failures, even at the very last step. Never let that bring your morale and will down. Learn from your rejections and mistakes, try to take feedback from interviewees, and work on that skill. Don't ignore your shortcomings; eliminate them one by one. Ignorance will only make you repeat them. Don't lose hope from your aspirations.
• Be curious, and stay updated. Always make sure you know what you're applying for. Read about the institution and its working. It is still good to be curious and ask interview questions that you have or share your perspectives. It is impressive to answer questions with practicality to their products and customer bases, and how it could be of use to the company. That kind of knowledge only comes when you spend time reading about the workings of the institution you want to interview for.

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