Data is the currency of the online world. If you know how to leverage data to understand your customers, it opens up a new world of unprecedented opportunities. Data Science, Machine Learning, NLP and Deep Learning are closing the gap between cognitive capabilities of humans and machines !

Srivatsan Ramesh, our next pathbreaker, works on developing non-contact, non-wearable devices(no camera or mic) that can monitor and analyse the health conditions of elderly people by analyzing data and building mathematical models .

Srivatsan talks to Shyam Krishnamurthy from The interview portal about his initial interests in solving math problems everyday that led to a career in Industrial Engineering and a rare opportunity to apply his love for maths in deep learning/image recognition for a fashion retailer and unstructured data mining for a AI startup.

For students, mathematics is the basis for scientific thinking, the explosion of data has brought back maths as a mainstream career to make sense of all the data that has been collected.The world needs more mathematicians in the future !

Srivatsan, tell us about your background?

I am Srivatsan Ramesh. I was born and brought up in Chennai, India. I had a very modest upbringing. My dad works for a finance firm and my mom is a home maker. I did my undergrad at SSN College of Engineering (Anna University) and went to grad school at Columbia University in New York.

Right from my middle school, I was passionate about math and I was hoping to pursue advanced studies in the same field. It was one of the key driving forces that helped me choose my major in college. 

Currently, I work as a Machine learning engineer in a startup called Tellus in San Francisco, California. My job is to build mathematical models for decision making.

Apart from work, I play cricket every weekend. I also learn boxing and it’s been really interesting.

What did you do for graduation/post graduation?

I did my Bachelor’s in Mechanical Engineering at SSN College of Engineering, Chennai . I pursued my Master’s in the field of Industrial Engineering and Operations Research at Columbia University, New York.

What made you choose such an offbeat, unconventional and cool career?

I thought to myself that if I could solve math problems every day, I would be the happiest person on this planet and that led me to choose this career path. I also had very supportive parents who let me follow my passion and never instilled their ideologies upon me.

How did you plan the steps to get into the career you wanted? Or how did you make a transition to a new career? Tell us about your career path

Mechanical Engineering can be divided into core Mechanical (like automobile, design, manufacturing) and Industrial Engineering (Supply Chain, Optimisation techniques). 

I was more interested in the latter and I was curious to know more about Supply Chain problems in an automotive industry. I pursued a couple of internships with Renault-Nissan and Ford motors in their Supply Chain department. At Renault, I worked on improving the efficiency of assembly line workers by understanding lag times at various assembly stations and recommending changes to the process. At Ford, I worked on a six-sigma black belt project where we had to reduce dents on door panels of Ford Eco Sport. It involved inspecting the process, identifying opportunities for improvement and suggesting methods to improve the process. At both my stints, I had to follow a data driven approach for recommendations and I realized the importance of data in an industrial setting. These experiences enhanced my liking for data driven approaches.

I had also worked on a research paper involving Supply Chain Optimisation where I had to design an assembly line depending on the available resources. I had to perform a lot of mathematical simulations to design an efficient assembly line. It was during that moment I decided to pursue my Masters degree in Industrial Engineering.

During my grad school, I was presented with an opportunity to work with Louis Vuitton (a leading fashion brand in the world) on solving one of their supply chain problems with image recognition. It involved working on an image recognition algorithm that can identify products displayed on the walls and recommend products to be kept on empty niches to improve profitability. It was my first experience with deep learning and I really liked working on complex math to find the right solution. It was during this time that I fancied working on machine learning problems that could shape the future and that got me to where I am today.

How did you get your first break?

It was through networking and some amazing friends I made during my internship at in San Francisco that led me to my first break at Tellus as a Machine Learning Engineer.

What were the challenges? How did you address them?

Some of the challenges I faced were due to lack of prior experience in this field. I competed with many people who had more than 2-3 years of work experience in this domain. So I had to work on a lot of industrial projects to make up for the work experience.

Where do you work now? 

I work as a machine learning engineer in a startup called Tellus in San Francisco. At Tellus, we work on developing a non-contact, non-wearable device(no camera or mic) that can monitor and analyse the health conditions of elderly people. For more information on our company, you can look at this link

As a machine learning engineer, you need to be good at coding and math. My typical day at work would be analysing data and building mathematical models that can aid in making decisions. I acquired coding skills during my grad school and I am working on making it better every day.

One thing I love about this job is the kind of challenges we tackle on a day-to-day basis and I also really care about the mission of our company that revolves around making elderly care better across the globe.

How does your work benefit the society? 

Our generation is moving towards a higher density of elderly people in the community. With rising elderly population, it’s hard to have a dedicated caregiver for each elderly person. I believe our technology can step in here and assume the responsibilities of a caregiver and make the elderly person feel more secured.

Tell us an example of a specific memorable work you did that is very close to you!

I am really fond of the work I did during my internship at has an automated assistant that joins a meeting via conference call (video or audio), takes notes and provides a summary of the meeting. I worked on improving the performance of the assistant by experimenting with various mathematical models. It was also a really small team (4 engineers) and that experience taught me many things about entrepreneurship.

Your advice to students based on your experience?

I would urge all students to follow their passion and never give up on their dreams. Life is all about living the moment and creating a path for yourself. Stay happy, Stay humble

Future Plans?

I like living the moment and giving my best. I hope it leads to something exciting in the future.