Data Scientist

Job description

About Lendable Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start: One of the UK’s newest unicorns with a team of just over 700 people Among the fastest-growing tech companies in the UK Profitable since 2017 Backed by top investors including Balderton Capital and Goldman Sachs Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot ) So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance . We get money into our customers’ hands in minutes instead of days. We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes. Join us if you want to Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1 Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo Build the best technology in-house , using new data sources, machine learning and AI to make machines do the heavy lifting About the role We are excited to be hiring a new Data Scientist into our team! Lendable is the market leader in real rate risk-based pricing, offering consumers transparency and product assurance at the point of application. Data Science sits at the heart of this USP, developing the credit risk models to underwrite loan and credit card products. You will have access to the latest machine learning techniques combined with a rich data repository to deliver best in market risk models. This role will primarily focus on our US unsecured loans and credit cards business. Our team’s objectives The data science team develops proprietary behavioural models combining state of the art techniques with a variety of data sources that inform market-facing underwriting and pricing decisions, scorecard development, and risk management. Data scientists work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, analyse and propose solutions. We self-serve with all deployment…