About Our Company
LemFi (YC S21, Series B) is revolutionizing cross-border financial services for immigrants through its multi-currency platform, processing over $1 billion in monthly transactions. We provide instant remittances, foreign exchange services, and multi-currency accounts, all in one seamless experience.
With 300+ employees across 15+ countries, our platform supports twelve currencies and integrates directly with local banks and mobile money providers, ensuring fast, low-cost transactions. But we're not stopping at payments. LemFi is building a comprehensive financial ecosystem empowering immigrants with the financial tools they need to thrive—wherever they go.
Our vision:
To build the first full-stack financial services hub for the world's immigrant population.
Who You Are
You are a candidate who would thrive in a fintech startup environment like ours, where we readily accept individuals with a humble, yet uplifting attitude alongside a diligent sense of work ethic. The teams here at LemFi are passionate about their work and fields of expertise, but also lend hands on cross-functional responsibilities to ensure the success of the company and the satisfaction of our clientele.
Job Summary
We're seeking a highly analytical and detail-oriented Lead Decision Scientist to own the development, deployment, and optimisation of credit decisioning and risk models. You will play a central role in shaping our lending strategy, building data products, and driving portfolio performance through data-led insight.
This role is ideal for someone with strong analytical and technical skills who thrives on data exploration, modeling, and experimentation.
Key Responsibilities
* Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data.
* Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring.
* Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience
* Analyse credit performance data to generate actionable insight and support strategic decisions
* Mentor and develop a small team of analysts/data scientists as the team scales
* Work closely with Data Engineering to deploy models into production pipelines.
* Collaborate with stakeholders to define modeling goals and interpret model outcomes in a business context.
Requirements
* 5-7 years of experience in consumer credit, particularly in a data science or decision science role.
* Hands-on experience building models in Python using libraries like scikit-learn, XGBoost, or LightGBM.
* Strong experience working with transactional datasets (e.g., Open Banking and Categorisation) and bureau data (e.g., Experian, Equifax).
* Deep understanding of feature engineering, data preprocessing, and dealing with class imbalance.
* Ability to evaluate models using appropriate metrics (e.g., AUC, KS, precision/recall) and validate across multiple segments.
* Familiarity with standard practices around model monitoring, performance tracking, and data drift.
* Strong SQL skills for data extraction, joining, and transformation.
Preferred Skills
* Familiarity with unsupervised learning methods such as K-means, DBSCAN, PCA, or autoencoders, and their application in credit use cases like behavioral segmentation, fraud detection, or exploratory analysis
* Experience working in a start-up or scale-up environment with fast decision-making cycles.
* Exposure to alternative data sources (e.g., device data, psychometric scoring) for credit scoring.