Lead Data Scientist

Lead Decision/Data Scientist – Credit London, UK – Hybrid Up to £100,000 Benefits About the Company We are working with a fast-growing fintech scaleup, who are on a mission to reshape access to financial services across underserved markets. With a strong focus on innovation, collaboration, and data-driven decision-making, the company is building impactful financial products tailored to real customer needs. We’re looking for a Lead Decision Scientist, to lead the development, deployment, and ongoing optimisation of credit risk models. This is a high-impact position at the intersection of data science, risk, and business strategy. You’ll drive lending strategy, own the end-to-end modeling lifecycle, and guide a growing team of analysts as we scale. This role is perfect for someone who thrives on experimentation, has a deep understanding of credit data, and enjoys working in a fast-paced environment. Key Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn, XGBoost, or LightGBM Strong SQL skills for data extraction and transformation Experience working with transactional (e.g., Open Banking) and bureau data (e.g., Experian, Equifax) Expertise in feature engineering, handling class imbalance, and evaluating model performance using AUC, KS, precision/recall, etc. Understanding of model monitoring and techniques for identifying drift Experience with unsupervised learning (e.g., K-means, PCA, autoencoders) for fraud detection or segmentation Exposure to start-up or scale-up environments Familiarity with alternative data for credit scoring (e.g., device data, psychometrics) If this role looks of interest, please apply here. Please note - This role cannot offer sponsorship.

Location: London,, GB

Posted Date: 8/1/2025