In this episode of Up The Ladder, Abhay Gupta (Cofounder of Board Infinity) will be joined by Mirza Rahim Baig (Analytics Lead at Flipkart). This article is the gist and it will be talking about Rahim’s incredible career journey, how freshers can get started in the data domain, current scenario of technology, and a lot more.

Coach Rahim has over 10 years of experience in advanced analytics, machine learning, consulting in the e-commerce and healthcare domains. He is also adept at machine learning, it’s applications and solving complex problems through data. He has been working at Flipkart for the past 5 years.

How was your transition from the field of electronics into data science?

Surprisingly, my first job was not in data science, it was in core electronics. When I joined the electronics company, I was very happy but realised soon that I cannot do this for the rest of my life. After a self evaluation of myself, I realised that I am more cut out towards problem solving with an inclination for numbers. That's the reason I knew I had to move out.

When I joined Zeus, it was more of a consulting role but I was exposed to a bit of data. While doing this I realised, this is interesting. It's not like I had a plan that I wanted to become a data scientist but it was a larger goal that I wanted to contribute towards problem solving. The power of data and the value it brings is what struck out for me. I fell in love with the mathematical and the statistical aspect of it.

From Zeus, I made a jump to IQVR. This is when I received more technical exposure. Statistical model, automated models, forecasting became my daily thing and that's when I realised, this is really fun.

Doing this for the rest of my life is something I could actually see for myself.  It was aligned to this goal and that is how I got my next job at Flipkart.

Where did you develop your passion for teaching?

My passion for teaching was already there. Even as a kid, at a very young age, I used to make the smaller kids sit down and teach them ABCD. Even during college days as well as before exams, I liked to help students out. During my free time, I used to teach the underprivileged kids.

Teaching has always been with me. It's a very satisfying and a fulfilling thing for me. Just like how a poet wants to do poetry, If I don't teach, it drives me crazy. If I learn something, I like to pass it on. Even at Flipkart, I was leading the learning charter.  This is the space where I can actually add value. I really enjoy teaching.

How has your role been working at Flipkart?

On one day you are working on one project and on another day you are handling something else. At Flipkart I started as category analytics. I was lucky to get there because that was the closest I could get to the business. That was where you learn the most about ecommerce in the shortest span of time. I got to work on the different parts of demand and supply. Then I worked on catalogue.

Thereafter, I started handling user generated content. And finally this year I have been handling and working with pricing. What I want to add here is that I am not building models all the time. I define my role as a business problem solver in data.

How do you ensure you are on the top of your learning requirement?

One is to identify your strengths and play by your strengths. The other is to upskill. But while upskilling it shouldn't be in any area whatsoever. Question you should ask is how it is helping you in the short term and in the long term. I choose only those things helping me in the short term and those that could help me become something in the long run, rest everything is just noise.

Are there any things you would have differently in your career?

A mistake, early on I think, I should have spoken to more people and broadened my thoughts as it would help me refine my career. Another thing, in the beginning of my career, I was very casual.

The biggest thing would be not engaging enough with professionals or role models. This could have added more value to my career.

What is the value of Data Science:

Even in 2014, the word term data science was not being used. This does not mean whatever is going on now did not exist earlier. The time we had numbers, data science began. During the World War, it became formalised on a higher scale.

The big picture today is the volume of data we possess and how it's exponentially increasing. Sadly, we are drowning with information but we are completely starving for knowledge. Businesses nowadays have terabytes of information but they don't know what to do with it. So that's the situation right now.

In this situation, if a person comes and tells you that If I can create business insights and make profits, that person automatically becomes valuable. Today, A small device you wear on your hand is generating so much information. So, Data science is here for a long time and it's here to stay.

The real definition of data science in the industry?

What people call a data scientist changes so much with the company, the industry and so on. For eg: I have a colleague who works at LinkedIn but till date has not worked on any models. But she has worked on another science. She is extremely good at using base statistics to test if something worked or not and that's big. Not everything is models. So she is a data scientist who has not built a single model. On the other hand, there is another data scientist who only reads research papers. So never go by title, always go by what the role is, what skills you need and that should be your guiding light.

How to make a switch to data science within the company?

Whatever job you are in, there is some exposure to data. Work with data, build an understanding. Speak to managers, let them know your interest in data science. Take this project on the side and do it during non-working hours. You never know, this project could be your ticket into the data side of things within the company. If that doesn't work out, then you should look for a change outside.

Money and increment should not matter at this juncture of time. Even if it pays less, don't worry about the level. Even if you have worked for 3 years, the soft skills you learn during this time is invaluable.

What are the key skills needed to excel in Data Science?

The key skills have nothing to do with coding or algorithms. What really matters is whether you can solve a problem given that amount of data. It may sound like a small thing but it encompasses a lot.

In one of the sessions at Board Infinity itself, we had to build a regression model. In three lines of code, the students built a model. If it is that easy, why is it difficult to get into data science? But the key takeaway is how do you think. What do you want to predict? What are the factors driving it? There are so many considerations around it. In a project to do with data science, the actual model is less than 10%. The other part is how we are solving it. Is it the right way we are looking at it? What makes a good data science professional in the long run is a structured problem solver. A general problem solving approach and a mindset is what you need to build.