Lead Data Scientist
2 days ago
City of London
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.