Analyst, Lending Models

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.

• 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 translate business needs into practical solutions, while complying with all mandatory policies and maintaining a strong internal control environment., At Allica Bank we want to ensure our employees have the right tools and environment in which to succeed in their role and in support of our customers.

• You have at least 5 years' hands‑on risk modelling experience, applying statistical modelling techniques such as logistic and linear regression, as well as machine learning models, to solve practical business problems.
• You are comfortable with large data sets, and data extraction and manipulation using Excel, SQL, Python & BI tools.
• You have a proven track…