Today in Accredian’s Success Story blog, we introduce you to Arun Mathur, who is an AVP at Retail Scan Management Services. He shares his journey of achieving success and how he transitioned into the Data Science domain. Get to know more about him as he guides us through this amazing process.
Q1 . Tell us about your current role and background.
I am Arun Mathur. So, precisely I was from an IT background and have worked in many multinational companies, 2-3 multinational companies as an IT Software Engineer, Analyst, and System Analyst.
But in 2010, I joined NielsenIQ, earlier it was Nielsen Global Consumer Business. Nielsen was a market research company and because of its market research domain, there was a lot of data science in use. So, that meant, knowingly or unknowingly, I just became a user of data science.
But slow and steadily, the interest rose. As you know, with the changing scenarios, I was looking into LinkedIn, and a lot of posts indicate that the technology is moving towards AI and Machine Learning. And what I realized is that the base of Machine Learning and AI is ultimately data science.
With whatever exposure I got over the period of 12 years, with my experience at Neilson. So, I decided to take formal training in data science because of my interest. I recently left NeilsonIQ and joined another market research company, of the same domain, but that is Retail Scan Management Services as an AVP Data Science and Visualization.
You will be surprised that there is a drastic vertical uplift in the roles, from the System Analysts to the AVP of Data Science.
Q2. What challenges did you face while exploring a Data Science role?
Understanding the domain of Data Science and all its branches combined was initially a huge challenge for me. There is data collection to data cleaning, EBA, ELP, etc. So, In order to become a complete Data Science Practitioner, I had to tackle this.
Q3. How has Data Science helped you in your new role?
Data Science, as I told you earlier had fascinated me, and I was willing to change from my programming background to the Data Science domain. I was highly motivated.
So I discovered that Data Science is the only role that suits me, by keeping the business domain the same but changing the work domain. That’s why I decided to go for data science.
Q4. What was the interview experience at your current company?
My interview experience at Retail Scan was great. They had done some preliminary interviews over phone interviews. For the second round, I was sent a link for an LMS sign up. I had to attempt a questionnaire which was time-bound.
And after that, in the third round, they had a face-to-face video interview which included a panel of six professionals who were data scientists and visualizers. I was mostly asked questions about my domain expertise and my past experience.
Q5. Why Accredian? How did it help you?
One of my colleagues told me about Suchit Majumdar, who was a mentor at Accredian. Right after which I was given a recorded video of Suchit, and I was highly impressed by that session. At that moment I decided to join the program at Accredian.
I joined in January 2022 for the Senior programs and I completed in June 2022. I must appreciate that in Accredian, the sessions were uploaded many of the, study materials that I found relevant and was really useful. But precisely, the introduction and lectures that Accredian provided with the help of Suchit Majumdar were tremendous.
Q6. What advice would you give to Data Science beginners?
The advice that I can only give is that although you enter into the data science field, you need to focus on the practical aspect as well because it is a vast domain with more elements involved at each step.
The moment you are involved with things like Big Data, Machine Learning, Data visualisation, etc., things might get complicated. So thorough practice and asking questions regularly will always help a lot.
If you want to be a part of the data science community and achieve something in the future, you guys should join Accredian for a better future and for achieving more in the field of data science.
We hope you found this success story interesting. If you have any Data Science questions, do check our Data science programs and we will get back to you.
