Data Scientist

Data ScientistAbout Lendable

Lendable is on a mission to make consumer finance amazing:faster, cheaper, and friendlier.We're building one of the world's leading fintechpanies and are off to a strong start:

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 bankswith dated systems and painful processes.

Join us if you want to
Take ownershipacross a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1Work in small teams of exceptional people,who are relentlessly resourceful to solve problems and find smarter solutions than the status quoBuild 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 techniquesbined 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 modelsbining 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 and monitoring, without a separate machine learning engineering team. Design, implement, manage and evaluate experiments of products and services leading to constant innovation and improvement.
How you'll impact those objectives
Learn the domain of products that Lendable serves, understanding the data that informs strategy and risk modelling is essential to being able to successfully contribute value. Rigorously search for the best models that enhance underwriting quality. Clearlymunicate results to stakeholders through verbal and writtenmunication. Share ideas with the wider team, learn from and contribute to the body of knowledge. Key Skills Experience using Python and SQL. Strong proficiency with data manipulation including packages like NumPy, Pandas. Knowledge of machine learning techniques and their respective pros and cons. Confidentmunicator and contributes effectively within a team environment. Self driven and willing to lead on projects / new initiatives.
Nice to have
Prior experience of credit risk for consumer lending or credit cards, especially for the US market. Interest in machine learning engineering. Strong SQL and interest in data engineering.
The interview process
Initial call with TA Take home task Task debrief and case study interview Final interviews with leadership team
Life at Lendable
The opportunity to scale up one of the world'smost successful fintechpanies.Best-in-classpensation, including equity. You can work from home everyMonday and Fridayif you wish - on the other days, those based in the UKe together IRL at ourShoreditch office in Londonto be together, build and exchange ideas. Enjoy afully stocked kitchenwith everything you need to whip up breakfast, lunch, snacks, and drinks in the office every Tuesday-Thursday. We care for ourLendies' well-beingboth physically and mentally, so we offer coverage when ites to private health insurance We're anequal-opportunityemployer and are looking to make Lendable the most inclusive and open workspace in London
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