Analyst, Lending Models

About Allica Bank Allica is the UK’s fastest growing company - and the fastest-growing financial technology (Fintech) firm ever. Our purpose is to help established SMEs, one of the last major underserved opportunities in Fintech. Established SMEs are the backbone of local communities - representing over a third of our economy - yet have been largely neglected both by traditional high street banks and modern fintech providers. Department Description The Modelling team sits within the Credit Portfolio Management (CPM) function and develops the measurement and decisioning capabilities that support Allica’s lending products. The team builds and owns the bank’s IFRS9 model estate, including PD, LGD, SICR and economic response models, as well as the statistical and machine learning models, credit policy rules and credit-linked GenAI solutions used to support and automate lending decisions. Role Description This role will be part of the team building, implementing, monitoring and optimising the models and policy rules behind automated lending decisions. These capabilities are critical to delivering fast, consistent and well-controlled credit decisions, improving the customer journey and managing credit risk as the bank grows. The role requires the technical ability and practical mindset to take models and decisioning changes from initial problem definition through data identification, preparation and analysis to model development, implementation and ongoing monitoring. The successful candidate will be comfortable moving between structured model development and fast-paced ad-hoc analysis, working closely with Credit Risk, Underwriting, Product, Data, Engineering and Model Risk colleagues. They will communicate complex findings clearly to stakeholders with different levels of technical expertise and work with third-party suppliers where required. We are looking for a hands-on modeller who can turn data into reliable, scalable decisioning solutions and continuously improve how the bank assesses credit risk. Principal Accountabilities Take a hands-on role in the design, build, implementation and continuous refinement of the statistical and machine learning models that support automated lending decisions. Develop and deploy credit risk models in Python across the full model lifecycle, including data preparation, feature engineering, model fitting, validation, implementation and ongoing performance assessment. Perform ad-hoc analysis to optimise credit policy rules and decisioning flows, using rule-firing, decision and outcome data to identify redundant, overlapping or mis-calibrated rules. Produce regular scorecard and decisioning monitoring covering population stability, discriminatory power, calibration, segment and vintage performance, decision rates, and alignment with underwriting outcomes. Automate monitoring packs and controls, including metrics such as PSI, Gini and KS, so that emerging performance issues and data-quality problems are identified quickly and consistently. Recalibrate or redevelop models as portfolio experience matures, comparing predicted and actual outcomes and adjusting model scaling, score cut-offs or policy thresholds where evidence of drift or mis-calibration exists. Lead root-cause investigations into model, rule or decisioning-flow underperformance by tracing issues through decision logs, data lineage, feature calculations, production pipelines and implementation logic. Build robust, well-controlled and reproducible modelling processes, and drive improvements to the data, tooling and infrastructure required for future model development and deployment. Document models, rules, assumptions, limitations and changes to a high standard, supporting governance, independent validation and stakeholder review. Work closely with Credit Risk, Underwriting, Product, Data and Engineering teams to translate business requirements into practical decisioning solutions, while complying with mandatory policies and maintaining a