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.