In Conversation With - Rohit Katariya (AVP Data Science), Naukri
Data science is massively popular today and rightly so! With the advent and popularity of digitization, data has become an integral part of all businesses, leading to a rise in the demand for Data Scientists.
But what happens behind the scenes and what does it mean to become a data scientist? We try to answer these questions and more with Rohit Katariya, Associate Vice President of Data Science at Naukri.
Hi Rohit, could you please tell us a little about what you do?
I am working as the AVP of Data Science at Infoedge - Naukri. I'm heading a team of Data Scientists working on projects on Search, Recommendations, Growth, and Assessments tracks.
What got you interested in the field of Data Science?
Data Science fascinated me because it is being used to solve varied real-world problems. It is being used in finance, image, and NLP and can be applied to improve all spheres of life.
Once you found your passion, what was your next step to make a career out of it?
I was in college when I realized my passion for the field. In my PG, I chose most of the courses that would help me understand the area better and prepare me for a job in the Data Science field. My M.Tech thesis also involved Machine Learning, which helped me get exposure to the field.
Please walk us through your journey so far.
My journey has been very exciting, I started working on libraries, then on suggesters, SEO, Job Search algorithms, and recommendations. Apart from these, I also got to improvise and build products for different businesses.
Now I am working with multiple teams on various projects, with young talent with incredible energy, which is exciting and challenging simultaneously.
What does a day as a data scientist look like?
Data science is a problem-solving field. We first analyze the data and try to get a feel of the problem. It involves a lot of product discussions to define the problem.
Once the problem is defined, we research and try to reuse an existing solution, or build a new solution that involves one or more stats, machine learning, and deep learning.
The role also consists of a bit of engineering work to produce the models we build. Data Scientist is a bit of a loosely used designation as different companies have varying amounts of the above-mentioned tasks.
What are the 3 things that you like & dislike about being a data scientist?
Likes:
- Working on the latest models and staying updated with the latest developments in the field. Things become outdated very soon.
- We get to drive many projects in internet companies like Infoedge, where Data Science has a high impact factor.
- A lot of inter-team interactions, where we collaborate with product, tech, sales, marketing, etc.
- Learning never stops.
Dislikes:
- Data science is a loosely used term, many organizations still confuse data pulling/extraction as data science. It is important that we create separate roles for separate jobs and have segregations so that job seekers have a clear understanding of the role.
How do you maintain a work-life balance?
I think work-life balance is mainly affected by the team's culture, set by the managers, and also by the nature of the business.
At Infoedge, I believe there is no issue with work-life balance across teams.
What would you advise our readers planning to pursue a profession in data science?
I think Data Science has a very bright future. As data is becoming the new oil, data scientists are the new refineries, providing value to the crude data.
My advice to aspiring data scientists would be to focus on the basics: Mathematics and statistics, the rest would come easy.
How has Info Edge helped in your career growth?
A company's biggest support to an employee is quality of work. I've got new projects at every stage of my tenure. We've had great mentors who've supported and guided us all along. What more can one ask for?
How has the field of data science advanced since the time you started? And what is the next big thing in the field?
It has become more interesting. We get new Machine Learning models every day, and getting new SOTA, it is impossible to keep up to the speed, but the basics remain the same.
There is no single way to solve a problem. When I started, we didn't have a single GPU; nowadays, all the model training is done on GPUs. We began with statistics-based relational modeling, and now with bigger modeling capabilities, we have 20 different ways to achieve better results.
What’s the success mantra of your life?
Keep calm, everything will fall into place, and for continuous success, keep learning anything related/unrelated to your work.
What is In Conversation with?
Seeking an expert's opinion is not always easy, so we are bringing the expert's opinion to you!
In this series, we aim to explore the career journey of people from different fields, backgrounds, and career stages who have tread the path and made a place for themselves in their specific fields.
Join us as we try to understand all about what they do, how they started, and where they are headed next.
Stay tuned to read more such articles.
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