Our Story
Hello there. We’re Zopa.
We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched Zopa Bank. A bank that listens to what our customers don’t like about finance and does the opposite. We’re redefining what it feels like to work in finance. Our vision for a new era of banking puts people front and centre — we’ve built a business that empowers everyone to aim high, every day, to move finance forward. Find out more about our fantastic offerings at
Zopa.com!
We’re incredibly proud of our achievements and none of it would be possible without the amazing team here. It’s not just industry awards we’re winning, we’ve also been named in the top three UK’s Most Loved Workplaces.
If you embrace unconventional challenges, are unafraid to think differently and are driven to make an outsized impact, you’ll thrive here at Zopa, so join us, and make it count. Want to see us in action? Follow us on Instagram @zopalife
At Zopa, data and the application of machine learning is at the heart of what we do and the products we bring to market. Within consumer financial services we have pioneered modern data science techniques using advanced ML models for more than 7 years.
Today more than 98% of our lending decisions are driven by ML models - so it's safe to say it is seriously impactful work!
As a Lead Data Scientist at Zopa, you will be leading high impact projects related to data and modelling, across a broad range of topics such as marketing, customer engagement, credit risk, fraud detection and pricing.
You will own the full lifecycle of your projects, including the discovery of business opportunities through statistical analysis, data curation and processing, feature engineering, development of machine learning model, deployment to production, and model monitoring. You will engage with senior stakeholders across the company, influence critical business decisions, and make direct impacts on our products and millions of customers.
On daily basis, you will work closely with product managers, analysts, data engineers and software engineers to make progress on your project. You will also support other data scientists by knowledge sharing, code review, collaboration on common utilities and analytical infrastructure.