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.
Our teams 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 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 these 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.
Clearly communicate results to stakeholders through verbal and written communication.
Share ideas with the wider team, learn from and contribute to the body of knowledge.
Key skills
1+ years of experience in a Data Science role
Experience using Python and SQL
Strong proficiency with PyData stack
Knowledge of machine learning techniques and their respective pros and cons.
Confident communicator and contributes effectively within a team environment
Self driven and willing to lead on projects / new initiatives
Nice to have
Interest in machine learning engineering
Strong SQL and interest in data engineering
Interview process
Initial call with the Talent Acquisition Team
Take home task
Task debrief and case study interview
Final interviews with Exec team
