Staff Data Scientist

Join LemFi as a Staff Data Scientist in London, playing a key role in shaping credit decisions and financial strategies for the Global South.

• A senior data scientist who combines strong technical depth with commercial judgement you care about shipping better credit decisions, not just building elegant models.
• Comfortable operating at staff level: you can set direction in ambiguity, influence senior stakeholders, and improve systems through judgment rather than waiting for perfectly defined scope.
• Scientifically rigorous but pragmatic, you know when a simpler model is the right answer, when experimentation is essential, and when operational constraints matter more than theoretical lift.
• Highly credible with both technical and non-technical audiences able to debate methodology with data practitioners and then explain trade-offs clearly to executives.
• Motivated by high-stakes, real-world decisioning where accuracy, fairness, and reliability matter because the product affects people's financial lives.
• Significant experience in data science, machine learning, or quantitative decisioning roles, with meaningful time spent in consumer credit, lending, fintech, or another closely related risk-heavy environment.
• Strong hands-on fluency in Python and SQL for model development, analysis, feature work, and investigative problem-solving.
• Deep experience building and evaluating predictive models in production, including feature engineering, validation design, calibration, monitoring, and lifecycle management.
• A strong grounding in credit risk concepts and portfolio metrics, including probability of default, loss behaviour, segmentation, and the practical use of bureau or alternative data.
• Experience partnering with engineering teams to deploy decisioning or ML systems into live products, rather than operating purely in offline research environments.
• A track record of turning analytical work into measurable commercial or risk outcomes for example improving approval quality, reducing losses, increasing conversion responsibly, or sharpening collections strategy.
• Strong judgment in regulated or high-accountability settings, where explainability, controls, and decision quality are as important as raw model performance.