Senior 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 is at the heart of building the measurement and decisioning models behind Allica’s lending products. The team builds and owns the bank’s IFRS9 model estate (including PD, LGD, SICR and economic response models), the statistical and machine learning models and credit policy rules used for automated underwriting, and the credit-linked GenAI solutions used to automate processes. Role Description This role will be part of the team building, implementing, monitoring and optimising two sets of models: the IFRS9 impairment models that measure our credit risk and support accurate reporting to auditors and investors, and the decisioning models and policy rules behind automated lending, which are critical to delivering fast, consistent and well-controlled credit decisions and a better customer journey 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, cleansing 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 Finance, Credit Risk, Underwriting, Collections, Product, Data, Engineering, Capital Markets and Model Risk colleagues. They will communicate complex concepts 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 solutions, and help make data a key strength for the bank. Principal Accountabilities Take a hands-on role in the design, build, implementation and continuous refinement of the bank’s credit risk models, spanning the full suite of IFRS9 impairment models (PD, LGD, SICR and economic response) and the statistical and machine learning models that support automated lending decisions. Develop and deploy 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 monitoring, covering population stability, discriminatory power, calibration, and segment and vintage performance, as well as decision rates and alignment with underwriting outcomes for decisioning models. 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 well-controlled, 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 review and validation, and stakeholder review. Work closely with Credit Risk, Finance, Underwriting, Collections, Product, Data and Engineering teams to track model requirements and translat