Senior Credit Risk Modelling consultant

MERJE; Greater London; Presencial; GBP 70.000 - 120.000; Jornada completa

Descripción de la vacante

MERJE is a fast paced consultancy delivering risk, data and applied AI in the regulated lenders sector. We seek a senior credit risk professional to lead modelling, validation and decisioning across IFRS 9, IRB, PD, LGD and ECL.

You will apply Python, SQL and ML techniques, work across portfolios, and explain complex models to non-technical stakeholders. A strong quantitative degree and hands-on experience are essential.

Formación

• Experience in credit risk, analytics, data science or quantitative research.
• Hands-on exposure to IFRS 9, IRB, scorecard development or model validation.
• Strong quantitative degree; MSc is a plus but not required.
• Strong Python and SQL skills.
• Clear written English for explaining models to non-technical stakeholders.
• Ability to work with light supervision and deliver on time.
• Genuine interest in lending regulation - IRB, IFRS 9 and PRA expectations.
• Adaptability to changing client needs and modelling tasks.

Responsabilidades

• Develop PD, LGD, EAD and ECL models across portfolios.
• Build and validate scorecards and decision engines.
• Calibrate, back-test and benchmark models.
• Collaborate with stakeholders and explain models to non-technical audiences.
• Support risk strategy and regulatory reporting obligations.

Conocimientos

Credit risk analytics

IFRS 9

IRB

Scorecards

Model validation

Data science

Python

SQL

Git

Excel

Cloud (AWS/Azure/GCP)

SAS/R

Educación

Mathematics/Statistics/Econometrics/Physics/Engineering/Computer Science/Finance degree

Herramientas

Python

SQL

Git

Excel

AWS/Azure/GCP

SAS/R

Descripción del empleo

A fast paced, forward-thinking consultancy. Delivering expertise in risk, data and applied AI, across the regulated lenders sector.

The role with entail:

SERVICE LINES

Credit risk modelling; IFRS 9 and impairment; IRB and capital; scorecards and decisioning; model validation, monitoring and model risk

REGULATION

IFRS 9; Basel 3.1 and CRR; the PRA IRB rulebook; EBA Guidelines on PD and LGD estimation; SS1/23 model risk management principles; Consumer Duty where it touches affordability

MODELS AND METHODS

PD, LGD, EAD and ECL; application and behavioural scorecards; logistic regression, GLMs, survival analysis, time series; gradient boosting and other machine learning; calibration, segmentation, back-testing, benchmarking and monitoring

PORTFOLIOS

Retail mortgages, unsecured lending, credit cards, motor finance, SME and specialist lending

TOOLS

Python (primary); SQL; Git; Excel to a high standard; exposure to AWS, Azure or GCP is a plus; SAS or R is useful where you have it

ADJACENT

Stress testing and ICAAP; credit strategy and affordability; COREP, FINREP and Pillar 3; data quality and lineage

WIDER FIRM

Data engineering, pipelines and data governance; machine learning development and deployment; applied AI strategy, tooling and safety

You will need:

• relevant experience in credit risk, analytics, data science or quantitative research.
• Hands-on exposure to at least one of IFRS 9, IRB, scorecard development or model validation, with the range to pick up the others.
• A strong degree in a quantitative discipline: mathematics, statistics, econometrics, physics, engineering, computer science or finance. An MSc is a plus but not required.
• Strong Python, with code you can talk us through. SQL to a working standard.
• Clear written English. A large part of the job is explaining a model to not technical stakeholders
• Evidence you can run a piece of work with light supervision and land it on time.
• Genuine interest in lending and its regulation - IRB, IFRS 9 and what the PRA expects.
• Adaptability. Our work changes shape between clients, and so does the modelling.