This article was first published on Insights – Ripple
As a relatively new technology, most people have a lot to learn about blockchain. But not everyone is happy to admit that they don’t know or understand how it works. Shae Wang is not one of those people.
With her background in statistics and engineering, Shae had spent a number of years in data science roles at more traditional tech companies. She did predictive modeling and data analytics for Uber, built neural networks for a trading startup and used machine learning to help a data consultant company predict a client’s movie streaming revenue.
When the opportunity came to join Ripple, she jumped at the chance despite not really understanding much about blockchain or cryptocurrencies. Fortunately, Shae loves to learn.
“I like it when my brain hurts from not understanding things,” she says, “It happens to me all the time working in blockchain. There are so many new and complex ideas to understand. But one thing I did know in advance is that data science is really underutilized in this industry.”
Shae is aiming to change that at Ripple by embedding data science into workflows throughout the product life cycle. The first step is designing and implementing a framework for running experiments and making causal inferences, which can help shorten user feedback loops and help the team build better products and services.
She is also driving a key sustainability initiative to quantify the environmental impact of payments, from cash and credit cards to cryptocurrencies like Bitcoin, Ethereum and XRP.
“It’s unlike any other data project I’ve done because there was a lack of existing research and very limited data to work with,” Shae explains. “When you look at the lifespan of a dollar, payment transaction or digital currency, it’s hard to pull together information about the entire supply chain. None ...
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