Lead Data Scientist (Credit & Lending)

About the CompanyWe’re partnering with a high-growth technology company building next-generation products at the intersection of AI, data and financial services.The company is developing intelligent infrastructure that uses sophisticated data and machine learning to improve how financial decisions are made.The RoleWe’re looking for a Senior Data Scientist – Credit Risk with strong experience within fintech lending or digital financial services.You will take ownership of developing and productionising sophisticated credit decisioning and risk models, with particular expertise in probability of default and related credit-risk methodologies.The ideal candidate combines deep credit-risk knowledge with strong machine-learning capabilities and a creative approach to data. This is a highly hands-on position where you'll become a key technical authority for credit-risk modelling within the organisation.What You'll DoBuild credit decisioning models — Design, validate and deploy probability of default (PD) and related models including LGD, EAD and expected loss, taking them from raw data through to production.Explore alternative data — Identify and engineer non-traditional signals alongside conventional financial and transactional data to improve model performance.Develop rigorous scorecards — Apply techniques including WOE/IV binning, monotonic constraints and reason codes, alongside robust discrimination and calibration methodologies.Own the model lifecycle — Manage training, tuning, out-of-time and out-of-sample validation, deployment and ongoing monitoring for model drift and stability.Drive model governance — Establish strong validation practices including champion/challenger methodologies, leakage detection and robust model documentation.Become the modelling authority — Partner closely with product, engineering and risk stakeholders to determine how credit models should be designed, calibrated and defended.Must-HaveStrong background within fintech lending or digital lending.Experience building and deploying production credit-decisioning models, particularly probability of default.Experience exploring alternative data, including behavioural, web or other non-traditional signals.Strong scorecard expertise: WOE/IV, binning, monotonic constraints and reason codes.Strong tabular ML experience, including XGBoost, LightGBM, CatBoost, logistic regression and tree ensembles.Deep understanding of model validation, calibration and hyperparameter tuning.Experience deploying models into cloud-based production environments.Strong experience working with messy, real-world datasets and identifying data/target leakage.Advanced Python, including pandas and scikit-learn.Ability to operate across both the technical and commercial sides of credit risk—you can explain and defend a model to senior risk stakeholders while remaining hands-on with the code.Why Join?Join an early-stage technology business tackling complex financial problems through AI, machine learning and advanced data science. You'll work closely with senior technical leadership and have significant ownership over the company's credit-risk modelling capabilities.