Want to shape the future of responsible lending at an early-stage fintech?
Enjoy owning credit models from development through to production?
Looking for the autonomy to influence lending strategy rather than just build models?
This is a 6-person fintech start-up that's recreating personal lending through salary-linked loans, creating a fairer, more flexible borrowing experience centred around affordability rather than traditional credit scoring.
With a live product, positive cash flow and fresh funding on the horizon, they're entering an exciting growth phase and are looking for a Senior Data Scientist to play a pivotal role in scaling their credit decisioning capability.
The Role
You'll join a small, high-impact team reporting directly to the VP of Credit, taking ownership of the company's credit risk modelling and decisioning capability.
Responsibilities:
- Building and improving credit risk and decisioning models using Open Banking, bureau and internal lending data
- Developing scorecards and machine learning models to support underwriting decisions
- Deploying, monitoring and maintaining models within AWS production environments
- Analysing portfolio performance, arrears, defaults and customer behaviour
- Working closely with Product, Engineering and Risk to operationalise models and improve lending strategies
You'll ideally bring:
- Commercial experience building credit risk or decisioning models within lending (insurance backgrounds may also be considered)
- Strong experience with scorecards, logistic regression and/or machine learning models
- Good understanding of how models influence real lending decisions and commercial outcomes
- Experience deploying and maintaining models within production environments
- Strong Python, SQL and AWS experience (particularly SageMaker and Lambda)
- Comfortable working in a fast-moving, hands-on environment with high ownership
- Excellent communication skills with both technical and business stakeholders
What's on Offer?
- Salary up to £70,000
- Founding equity
- Hybrid working (2 days per week in Central London)
Apply below if interested.