FORESIGHT(2023): Summer Internship at AWL Inc. | Manav Nitin Kapadnis |

1) Brief Introduction

Greetings, my name is Manav Nitin Kapadnis. I am a fourth-year Dual Degree undergraduate student studying Electrical Engineering at the Indian Institute of Technology Kharagpur. This summer, I will be interning at AWL Inc. as an AI Engineer. I have gained valuable experience through previous internships with renowned institutions like University College London, University of Alberta, Rutgers University, and Massachusetts Institute of Technology. My research interests focus on the practical applications of Machine Learning and Natural Language Processing, particularly in the areas of Natural Language Processing, Graph Machine Learning, Representation Learning, and Multi-Modal Learning.

2) How did you get into AWL? What was the selection procedure?

I joined AWL Inc., a Japanese Deeptech startup specializing in Computer Vision and Deep Learning-based video analytics solutions, as an AI Engineer Intern. The selection process involved the following steps:

Resume Selection Round:

To progress to the next stage, it was crucial to have a strong background in Deep Learning and Machine Learning, supported by a few notable projects. While there was no specific CGPA cutoff, having a CGPA above 8 and an impressive resume significantly increased the chances of moving forward. Alternatively, even with an average CV, a CGPA above 9 provided a favorable opportunity.

Coding, Aptitude, and Data Science MCQ Round:

This round lasted approximately 1.5 hours and included a mix of two coding questions, around 10–15 multiple-choice questions related to Data Science, focusing on probability, statistics, computer vision, and deep learning. Additionally, there were MCQs assessing knowledge of Data Structures and Algorithms. Qualifying this round was essential to proceed to the final interview round.

Final Interview Round:

The interview round primarily revolved around discussing the projects and internships mentioned in the resume. The interviewers delved into the details of representative internships or projects, seeking explanations about the motivation behind them, encountered challenges, and how they were overcome. Furthermore, a case-study test was conducted, where I was asked to provide solutions to a problem the company was facing with their current product. This round typically lasted around 30 minutes, emphasizing the need to present one’s best project for discussion, as it played a significant role in shaping the final decision.

3) How to prepare for them?

I had mainly prepared for Data Roles in CDC. To prepare for the selection process, I followed a structured approach focusing on specific areas:

Data Structures and Algorithms (DSA):

I solved a variety of coding problems from InterviewBit, ensuring I covered the majority of the practice questions. In the weeks leading up to the selection process, I revised the challenging questions to reinforce my understanding. Although DSA wasn’t my primary focus, I made sure to cover the essential concepts necessary for success.

Machine Learning (ML):

I found Krish Naik’s YouTube playlist on Complete Machine Learning to be an excellent resource for gaining in-depth knowledge of various ML algorithms. This playlist also provided coding examples to understand and implement the algorithms from scratch. Additionally, I discovered the StatQuest YouTube channel, which offers easy-to-follow explanations of fundamental concepts. This channel also has a playlist specifically covering Probability, which was helpful for strengthening my understanding. For practice questions, I utilized Analytics Vidya’s top N questions for ML and Data Science interviews.

Probability and Statistics (Prob Stats):

I had previously taken a course on Probability and Statistics (MA20104), which provided a theoretical foundation. Alternatively, I recommend the Stat 110 course offered by Harvard University, which is available for free on Edx or YouTube. For practice, I used assignments from Stat 110 and questions from the book “Fifty Challenging Problems in Probability.” If additional practice was needed, I referred to InterviewBit’s DS and ML practice questions.

CV Preparation:

For each section of my CV (projects, internships, positions of responsibility), I created a comprehensive list of potential interview questions and prepared answers in advance. I found it helpful to make concise notes for each heading, which I could quickly review the day before the interview.

Please refer to the following link for a Google Doc containing a collection of resources I used for preparation: [Resource Doc Link:

https://docs.google.com/document/d/1YqjIMPHBfa_ioSsoaKN9R79xsUKQD3aIlIGo5SUGX_0/e dit]

4) What difficulties did you face while preparing for this Company/Profile? How did you overcome this problem?

Difficulties faced and tips to overcome them:

- Comprehensive Data Topics: Data encompasses a wide range of topics, making it challenging to brush up and revise everything just a day before the interview. To overcome this difficulty, I maintained a Notion board or a similar tool to track the topics I had covered and those that still needed review. This helped me stay organized and focused during my preparation, ensuring I covered all essential areas over time.

- Balancing CDC Preparation and Internship Work: I faced the difficulty of managing my CDC preparation alongside the responsibilities of my internship, particularly when I was visiting Canada. To address this challenge, I recommend starting the preparation process from mid-May if you are interning during the summer before CDC. Alternatively, if you have one month before the CDC process begins (typically in July), it is still feasible to prepare effectively, provided you have already been practicing Competitive Programming (CP) during the previous semester. Efficient time management and prioritization are key to maintaining a balance between internship work and CDC preparation.

- Finding Practice Questions for ML Case Studies: I encountered difficulty in finding sufficient practice questions specifically focused on ML case studies. To overcome this challenge, I relied on reputable platforms such as Analytics Vidya, Interviewbit, and MLExpert. These platforms offer a wide range of questions that help build a strong conceptual and practical understanding of the concepts required for data roles. Exploring their resources and regularly practicing with their case study problems can enhance your preparation for ML-related interviews.

By implementing these tips, you can navigate the difficulties faced during the preparation process and optimize your chances of success in the selection process.

5) According to you, who should ideally apply for this job?

Ideally, individuals who have a genuine interest in the field of Machine Learning and Data Science should apply for this job. If you have a passion for Data Science and are seeking an opportunity to work in a fast-paced international startup environment, AWL Inc. would be the ideal place for you.

6) Any specific advice you want to give to the junta sitting for internships this year?

- Research Companies: Take the time to research and understand the companies you are interested in. Explore their products, services, and values to ensure they align with your career goals and interests.

- Build a Strong Resume: Tailor your resume to highlight relevant skills, experiences, and projects. Focus on showcasing your achievements and the impact you made in previous roles or academic projects.

- Practice Interview Skills: Brush up on common interview questions and practice your responses. Be prepared to discuss your experiences, projects, and technical knowledge. Mock interviews or interview preparation workshops can be helpful.

- Stay Positive and Persistent: The internship application process can be competitive, and rejections are common. Stay positive, learn from each experience, and keep trying. Persistence is key to finding the right opportunity

- Seek Guidance: Reach out to mentors, professors, or career counsellors for guidance and support throughout the application process. They can provide valuable insights and advice to help you succeed.