Now that we live in a world where everything is more or less data-driven, a lot of organizations want to be on the top of their game and build a strong data science team. While the ‘why’ aspect of building such a team is understood, the ‘how’ part is what is tricky. We got in conversation with Amit Bendale, the Head of Data Science at Bright and this is what he had to say...
Why is Data Science so important in a Consumer Tech company today?
“Nowadays, there is a great need for data functionalities to answer business questions. We require predictions, recommendations and good data insights that can be put to refine what we offer to our customers. We want intelligent and automated systems, and all of this cannot be achieved without the power of data science.”
“The real innovation and creativity happens in data science. This is definitely the key market differentiator for any startup like ours, or even any company for that matter. Personally, I am of the opinion that we cannot create the ‘wow factor’ for our customers without the application of data science.”
“Data is the oil and data scientists are the best oil engineers out there. Their importance is only growing. A lot of manual jobs in the world today are being taken over by automated systems and the power behind those automated systems is data science. It promises a better future.”
What is the role of a Data Scientist in a Consumer Tech Financial company? What types of problems does a Data Scientist have to solve?
Particularly in a fintech, a data scientist needs to gather domain knowledge externally through market research and general research. Data should be looked at to solve business problems and business requirements need to be met by coming up with hypotheses which can be tested using this data.
This could be proved or disproved, but the capability of coming up with solutions is imperative. The data scientist must devise automations that are intelligent whenever required. It’s not just about creating “cool” machine learning or AI models, a good data scientist will be able to create AI systems in which models operating in production form the core. Keeping this in mind, we should remember that a data scientist needs to be an all-rounder in the truest sense (analyst, designer, builder, implementer, debugger, and quality-assurer). That is what will make him or her a huge asset to the company.
In your words, what does it mean for a Tech company to be Data Science first?
A tech company is data-science first only if it has a product centred around data science. It has to be invented, inspired and run by data science. There needs to be a dedicated data science team to run it.
For instance, at Bright, we have the ‘Debt Manager’, a product which was created by the data science team and that is what makes us a data-science first company. Usually, what companies do is that they put together a data science team once they have a large chunk of data. In such a case, data science becomes a research-based department when it should be an integral part of the company.
In a data-science first company, the data science team forms the core and the essence of the organization. That is what gives an edge to such companies when compared to its competitors.
How does Bright in particular live the values of being a Data Science first company?
Our first product at Bright was born out of data science, as mentioned earlier. Bright has given its data scientists the power to put AI components into production. The data science team today can create an end to end impact as it reaches the user too, in a self-contained manner.
In general, our business decisions are majorly data-driven. It is the bread and butter for the data science team, and we have been pro-data since the time of inception. We want to continue being driven by data and uphold our values of being a data first company.
What do you imagine the impact of Data Sciences at Bright will look like in 5 years? How would you describe it?
At Bright, we have 20+ components in the data science system today. Most of these are AI-based. In terms of real impact, the Debt Manager has reached thousands of users now and we are still seeing fast month-on-month growth in that number. It has created a retention level that is 10-20% higher than the average fintech product.
This is quite big compared to other fintechs today that are doing something similar to us. Customer reviews show how impressed our users are with their experience of our product. We have started a new credit lending track where data science is driving the model part of underwriting.
In the next 5 years, it’s evident that data science will be at the heart of every product driven by Bright. Without intelligence and intelligent systems, we will not maintain that competitive edge that we possess today. Data science brings something unique to the table.
Knowing that data science makes the lives of the users easy by reducing their burdens with respect to decision making, calculations and data crunching, is a truly satisfying feeling for us. Retention and impact will be much more in the future. The innovation that Bright will bring will surely draw users towards our products even more than they do now.