Quant Analyst - Credit Risk Modelling

MAZARS UK; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading global professional services network is seeking a Quant Analyst at the AVP level in Credit Risk Modelling. You will engage with banks, working on critical projects involving market, counterparty, and climate risk. The ideal candidate holds a Master’s in quantitative disciplines, has experience in credit risk modelling, and is proficient in Python, R, or C++. You will contribute to diverse teams delivering quantitative finance projects and participate in business development efforts.

Qualifications

• Experience in credit risk modelling (IFRS 9, IRB modelling).
• Strong knowledge in derivative pricing and quantitative risk management.
• Ability to work in a team and interpersonal skills.

Responsibilities

• Deliver quantitative finance projects for clients.
• Perform model validation for quantitative risk management models.
• Support business development by preparing client proposals.

Skills

Credit risk modelling

Derivative pricing

Python

R

C++

Stochastic calculus

Quantitative risk management

Education

Master's degree in quantitative discipline (e.g. mathematics, statistics, quantitative finance)

Job description

Quant Analyst (AVP Level) - Credit Risk Modelling (5271)

Forvis Mazars is a leading global professional services network providing audit & assurance, tax, and advisory services. Forvis Mazars in the UK spans 14 offices across the nation and has over 3,400 professionals, with 190 partners. We have a clear purpose and a shared commitment to shape a better future.

You’ll join a collaborative and inclusive team where you’re supported to grow your skills, explore new opportunities, and contribute from day one. You’ll work with a diverse client base, develop meaningful connections, and gain experience that extends beyond your local team. Together, we grow, belong and impact.

Job Purpose

Within the Quantitative Finance team of the Risk Consulting department, you will interact mainly with banks on a variety of projects related to Market Risk, Counterparty Credit Risk, Credit Risk and Climate Risk. You will hold or about to hold a master's degree in Quant Finance, Mathematics or Statistics.

Job Role

• Contribute in small and large-sized multidisciplinary engagement teams delivering quantitative finance projects for clients
• Cross-asset derivative pricing including valuation adjustments (XVA). Calibration of models using best industry practices
• Model validation for small to large size clients, for quantitative risk management models such as (PD/LGD, VaR, Expected Shortfall, EPE/PFE)
• Implementation review of accounting standards such as FRTB, IFRS9, CECL
• Development of internal pricing libraries and tools (e.g. C/ECL, stress testing)
• Oversee summer internship projects
• Contribute to Mazars' regulatory watch activities by writing articles or providing technical content
• Support business development by preparing client proposals
• Help with administrative tasks (such as training and recruitment)

Person Specification

• Must have experience in credit risk modelling (IFRS 9, IRB modelling)
• Holds a 2.1 or above Master's degree in a quantitative discipline e.g. mathematics, statistics, quantitative finance
• Advanced knowledge in derivative pricing, quantitative risk management (covering credit, market and counterparty risk), stochastic calculus, modelling, statistics and probabilities
• Strong significant experience either in derivative pricing, credit (PD and LGD modelling) and market (VaR, Expected Shortfall, FRTB) risk modelling
• Strong experience in either of Python, R or C++Ability to work in a team
• Desired experience/skills: model validation and machine learning

Diversity, Equity & Inclusion

At Forvis Mazars diversity, equity and inclusion are central to our values. We value our people's unique backgrounds, perspectives, and experience, and know this diversity create better outcomes for our clients.

We seek to attract, develop, and retain the best talent, inclusive of sex, ethnicity, disability, socio-economic background, sexual orientation, gender identity, nationality, and faith.

We select candidates based on skills, knowledge, qualifications, and experience and aim to support all our team members to reach their potential.