We’re a fast-growing FinTech building fairer and smarter lending solutions. Using advanced machine learning and rich financial data, we create credit products that improve outcomes for individuals and small businesses. Our mission is to make lending transparent, responsible, and accessible—and our models are at the heart of how we achieve this.

Joining us means you’ll be part of a collaborative, hands-on team where your work directly contributes to how we assess and manage risk.

We are looking for a Graduate Data Science / ML Engineer to join our credit risk team. You’ll be an independent contributor, taking ownership of model design, testing, and feature development, while learning from and working under the oversight of our Lead ML Engineer. This is a great opportunity to grow your technical skills, gain fintech domain knowledge, and have your work make a measurable impact from day one.

• Build, test, and iterate on machine learning models for credit risk, using Python and tools such as XGBoost.
• Independently explore new features and data sources to improve model accuracy and fairness.
• Use BigQuery and Google Cloud Platform (GCP) to work with large-scale datasets.
• Contribute to model explainability and support communication of results to credit analysts.
• Research, prototype, and present new ML techniques that could enhance model performance.
• Collaborate closely with data scientists, credit analysts, and engineers to deliver improvements end-to-end.
• Work with guidance and code reviews from the Lead ML Engineer, ensuring continuous learning and professional growth.

• Degree (BSc, MSc, or PhD) in a quantitative field such as Mathematics, Statistics, Computer Science, Data Science, Physics, or Engineering.
• Strong mathematical/statistical foundation, with an interest in probability, optimisation, and modelling.
• Proficiency in Python and an eagerness to deepen ML expertise.
• Familiarity with SQL; BigQuery experience is a plus.
• Curious, hands-on mindset—comfortable experimenting, iterating, and learning quickly.
• Strong communication skills: able to collaborate effectively and explain findings to colleagues from different backgrounds.

• Experience with ML frameworks (e.g., XGBoost, TensorFlow, PyTorch).
• Prior exposure to cloud environments (GCP preferred).
• Internship, dissertation, or project work involving applied machine learning.
• Knowledge of data visualisation libraries (matplotlib, seaborn) or BI tools (Power BI).

• Responsibility for independent contributions with structured support from an experienced ML Lead.
• Exposure to cutting-edge, real-world fintech models from day one.
• A collaborative environment where growth and experimentation are encouraged.
• Competitive salary, stock options, and benefits.
• Hybrid working with flexibility.

1. Recruiter Call – Initial introduction
2. Technical Interview – Applied coding and problem-solving
3. Take-Home Task – Practical ML/data challenge
4. Final Interview – Focus on growth potential, collaboration, and values fit