Analyst

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.

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.

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 strong internal control environment.