Scaler DSML Review | A Razorpay Product Analyst's Take
I’m Madhuri Kukreja, and I work as a Product Analyst at Razorpay. I recently visited the Scaler campus to answer questions about data science that people had shared on social media.
I get asked a lot of questions about data science, especially now that AI has become such a big part of the conversation. What should you actually learn? How do you make decisions when the data isn't clear? And once you start working as an analyst, what really makes a difference?
We spoke about everything from structured thinking and real-world data science to the tools I use to make my work easier. Some of these questions made me think about how I approach my own work too, so let's get into them.
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+1000 moreA Razorpay product analyst answers your data science questions
When I walked into the Scaler campus, I knew I was there to answer questions about data science, but I didn't know exactly what people were going to ask me.
The questions had come from people on social media, and they covered a pretty wide range. Some were about building projects, some about making decisions with data, and others were about the skills you actually need to become a good analyst.
I was a little nervous before we started, which probably isn't surprising when you're answering questions on camera. But once we got going, the process went on smoothly.
The questions covered different ways of working with data and how you would approach it in different situations. I enjoyed answering them because getting into the problem itself helps you understand which skills you need to work on, especially in a technical field like data science.
So here are some things I covered!
"Trust the numbers or your gut?"
When you're working with data and the numbers don't seem clear, I don't think the answer is to simply trust the numbers or go with your gut. I would do neither.
The first thing I would do is understand the data properly. What is it actually telling me? Is there something I haven't looked at yet? Before making a decision, I want to understand what is behind the numbers rather than immediately accepting them.
Once I understand the data, I try to put it into a structured insight that I can explain to other people.
That's important because your data should be able to tell a story. If someone asks me why I reached a particular conclusion, I should be able to show them how I got there and make it easy for them to follow the same story.
If I simply say, “I went with my gut,” that doesn't really help anyone understand the decision. There needs to be something behind it.
For me, good analysis is about taking what the data is telling you, putting it into the right structure, and turning it into an insight that is easily understandable.
The most underrated skill: structured thinking
I think structured thinking is the most underrated skill for an analyst. There are a lot of tools that can help you get a job, but knowing the tools isn't enough when you are given a problem to solve. You need to know how to approach it, how to work through it and how to do the RCA.
That is something I say people should focus on while learning analytics because tools will keep changing, but you will keep coming across problems where you need to understand what is happening, work through the different possibilities and find the reason behind it.
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I think companies can see a profile like this as a good fit, especially for a company looking for someone who understands both the technical and business side.
If you've worked with data and business together, you can bring both into your role. You know how to manage and instrument the data, but you also understand the business and how to use the insights from that data.
So I would put both sides of the experience forward and not worry about choosing one. You can show that you have the technical skills to work with data and that you understand how those insights can be used to drive the business.
What data science actually solves, day to day
When people hear “data science,” I think they sometimes imagine something very complicated happening somewhere. But a lot of the things you interact with every day are already because of data science.
Take Instagram or YouTube, for example. The recommendations you get after liking or clicking on ads happens because of set algorithm. Data science plays a role in understanding what people watch, what they interact with, and what content they might want to see next.
You can see the same idea in companies like Zomato and Swiggy. Data science can be used to manage and optimise their supply chains and drive insights with the help of huge amount of information involved in running those platforms.
So when someone asks me where data science is actually used in the real world, I don't think you have to look very far. A lot of the technology we interact with every day sets an example on its own.
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"There are no hacks. You need to work hard."
I get asked about hacks quite often, but honestly, I don't think there are any real hacks when it comes to data science. You need to work hard and understand what you're doing.
That doesn't mean you have to do everything from scratch. There are plenty of tools that can make the work easier, but you need to also understand which tool is right for the problem you're trying to solve and see how it works.
For coding and understanding problems, I use AI tools like ChatGPT and Claude. They can be helpful when I'm stuck or need help working through something, but I still need to understand the problem and the code I'm working with.
For visualisation, tools like Looker, Tableau and Power BI are useful depending on what I'm trying to present. And when I want to write code in a more structured and effective way, I can use VS Code.
There are also plenty of libraries available that can help with specific functions instead of making you build everything yourself.
So I wouldn't call these hacks. They're simply tools to make your work more efficient. The skill you need to work on is knowing when to use them, how they work, and how they fit into the problem you're trying to solve.
Can you build a chatbot with feelings?
Yes, you can explore the idea of building a chatbot that responds in a more human-like way. I actually think it's a great project idea if you're interested in AI and want to understand how all the pieces come together.
But also you should keep in mind that you would need to develop various set of skills, i.e, the frontend and backend, along with LLMs, machine learning, and neural networks.
Once you have those pieces in place, you can start putting the project together and experiment with your own ideas. There are also plenty of open-source resources available that can help you get started.
So if you're thinking about building something like this, I'd say go for it. Just be prepared to learn quite a few different things along the way.
FAQs
Can I become a data/product analyst without prior analytics experience?
You don’t have to worry about having prior experience in your initial stages. From my perspective, structured thinking, problem-solving, and knowing how to do root-cause analysis are some of the most important skills for an analyst. So, once you have these skills, you can always start applying for jobs in this domain.
What skills does a Razorpay Product Analyst actually use?
Structured problem-solving and RCA are important parts of how I approach analytics. There are also tools that can make the work easier. For visualisation, I use tools such as Looker, Tableau and Power BI, while VS Code can help with writing structured code. AI assistants such as ChatGPT and Claude can also be useful for coding support and understanding problems.
Is Scaler DSML worth it for an analyst career?
For Madhuri, Scaler was also about the support she received during the learning process. In her LinkedIn testimonial, she describes meeting Shivam Prasad as a “game-changer” and talks about how his support helped her deal with negativity and self-doubt. She also credits that experience with giving her more confidence and optimism to keep working towards her goals.
Read More at: Madhutri Kukreja's LinkedIn Post
Do analysts rely on gut feeling or data?
I wouldn't blindly rely on either. First, understand the data and what it's actually telling you. Then structure it into an insight that you can explain clearly to other people. Your data should tell a story, and someone looking at your analysis should be able to follow how you reached your conclusion.
If you're looking to build your skills in data science and machine learning, you can explore the Scaler Data Science & Machine Learning program to learn more.
