Staff Data Scientist

LemFi (Series B) is building the go-to financial app for the Global South. Moving to a new country shouldn’t mean starting from zero. That's why our team of 400+ spanning 20+ countries is building a financial ecosystem that helps immigrants stay connected to home, build stability, and create wealth regardless of where they are from or where they live. What began as fast, affordable remittances is now evolving into a complete platform for multi-currency accounts, payments, credit, and long-term financial growth. With millions of users across the globe, we process over $1B in monthly transactions to 30+ countries, proving that borders shouldn't limit financial opportunity.

The team you'll be joining and the impact you'll have: You'll sit at the heart of LemFi's Credit function as the senior-most individual contributor for data science in the domain partnering across Credit Risk, Product, Engineering, Analytics, and Commercial teams to shape how we underwrite, monitor, and scale lending. Your work will directly influence the quality of lending decisions, portfolio performance, and customer outcomes from improving approval logic and risk segmentation to identifying where the product can responsibly grow. You'll help define the scientific and technical standard for credit decisioning at LemFi bringing rigour to experimentation, model development, monitoring, and the translation of insight into production systems. This is a high-leverage role with real business consequence: your recommendations will inform decisions made by senior leadership on risk appetite, market expansion, and the long-term trajectory of LemFi's credit products.

What you'll be doing from day one: Owning the end-to-end data science strategy for Credit identifying the highest-value opportunities across underwriting, pricing, line assignment, fraud/risk interaction, collections optimisation, and lifecycle decisioning. Designing, training, validating, and iterating on predictive models that improve credit outcomes, using a strong mix of statistical thinking, machine learning, and practical judgement in a regulated environment. Working closely with Data Engineering and software engineers to productionise models cleanly ensuring features, decision logic, and monitoring are reliable, version-controlled, and scalable. Partnering with Product Managers and Credit leaders to translate ambiguous business problems into measurable hypotheses, model frameworks, experiments, and decision proposals. Building robust monitoring frameworks for model performance, drift, fairness, and operational impact with a high bar for explainability and governance. Leading deep-dive analyses into portfolio behaviour, repayment patterns, loss drivers, and customer segments to uncover actionable opportunities beyond surface-level metrics. Acting as a technical mentor and thought partner to analysts, data scientists, and cross-functional stakeholders raising the bar on problem framing, methodology, and decision quality.

Who you are: 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.

What you'll bring: 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.

Why Join LemFi? Love shouldn’t be expensive, yet those working hardest for their families often face predatory fees and banking exclusion. We're changing this. At LemFi, you won’t be just a cog in a machine. Whether designing products, scaling operations, or telling our story, you’ll tackle complex challenges with real, immediate impact. Your work goes beyond metrics - it puts money back in families’ pockets and offers access to the previously excluded. Join us to make a meaningful difference, where high performance is a lifeline for millions.