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Milton Keynes, England, United Kingdom
Hybrid
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.
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 translate business needs into practical solutions, while complying with all mandatory policies and maintaining a strong internal control environment.
Personal Attributes & Experience
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 record with Python coding, and are comfortable using GenAI tools to increase coding productivity.
You have a good understanding of IFRS9, credit and lending concepts, preferably in commercial lending.
You are driven, self-motivated and passionate about building great solutions for the bank.
You can work independently and are flexible to adapt quickly to changing priorities within a very dynamic environment.
You work well with stakeholders across the business, and collaboratively within the team.
You have strong skills in writing technical documents as well as business-focused documents to get buy-in from various stakeholders.
Experience of implementation of models into software applications is highly desirable.
Working at Allica Bank
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.
Our employees are at the heart of everything we do, so our benefits are designed with you in mind:
Full onboarding support and continued development opportunities
Options for flexible working
Regular social activities
Pension contributions
Discretionary bonus scheme
Private health cover
Life assurance
Family friendly policies including enhanced Maternity & Paternity leave
Don’t tick every box?
Don’t worry if you don’t have all the skills or requirements listed on the job description. If you think you’ll be a good fit, we’d still love to hear from you!
Flexible working
We know the ‘9-to-5’ isn’t right for everyone. That’s why Allica Bank is fully committed to flexible and hybrid working. Please let us know what is best for you and, if we can, we will do our best to accommodate.
Diversity
We’re a diverse bunch here at Allica, with all kinds of experiences, backgrounds and lifestyles. Our openness and differences make us stronger, and we want everybody to feel comfortable bringing as much of themselves to work with them as they like.
