Key Takeaways
Data science is a way to provide certainty: Credit Karma's recommendation engine is at the heart of its business models. With the use of data science, Credit Karma is able to increase financial inclusion by providing members a financial roadmap that takes them from where they are, to where they want to be.
Empathy is foundational to delivering data products that scale: Empathizing with members and the different backgrounds, credit scores, and their socio-economic status, was the key to scale to more than 100M+ members.
As you scale a data team, hire more specialists: As your data team matures, consider having a blend of generalist and specialist data scientists on your team. Moreover, provide upskilling pathways for your data scientists to specialize in one area of your tech stack.
Key Quotes
Let's say you open up Google Maps and you are trying to get somewhere. The first thing you’ll do is provide a starting point, and a target destination to go to. The certainty of Google Maps leaves no room for confusion. For many Credit Karma members, the starting point is where they stand with respect to their credit history, credit report, and credit score. That's just such a powerful data point when you put it in front of an individual because you are no longer at the mercy of credit providers. You have a good sense of what you're allowed to do at that point in time, and with data science, we’re able to provide them with a roadmap to reach their destination.
It's really important to stay humble and curious and to understand how users experience the data products you develop. If your recommendation engine sends out an occasional bad recommendation, you might think that "Oh, of course, I'm doing this for 100 million users, you can expect me to get a few users here and there wrong." But if you lead with empathy with the user, you’ll be able to eliminate a lot of these wrong recommendations.
