National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Assistant Vice President, Model Risk Quantitative Analyst

MUFG
London
5 months ago
Applications closed

Related Jobs

View all jobs

Enterprise Risk Data Analyst, Senior Vice President

IT Assistant Vice President – Data Engineering

Assistant Group Head of IT

Assistant Professor of Data Science in Computer Science (Tenure Track)

University Assistant Professor in Responsible Machine Learning

Research Assistant in Data Science

Assistant Vice President, Model Risk Quantitative Analyst

Apply locations London time type Full time posted on Posted 30+ Days Ago job requisition id 10066636-WD

Do you want your voice heard and your actions to count?

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), the 7th largest financial group in the world. Across the globe, we’re 120,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.

With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.

Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.

OVERVIEW OF THE DEPARTMENT/SECTION

Enterprise Risk Management (ERM) is responsible for supporting the EMEA Chief Risk Officer to implement an effective risk governance framework across MUFG EMEA, and providing a holistic view of the risks facing MUFG in EMEA, including environmental and social risk management.

The EMEA Model Risk Management (EMRM) within ERM is responsible for model governance and the validation of models used by MUFG in EMEA. This includes, among others, derivative pricing models, risk models used for risk measurement and decision-making purposes, capital models, AI models, etc.

EMRM works closely with all stakeholders including Risk Analytics and Front Office quants to ensure that all models are validated on a periodic basis as well as at inception and changes. EMRM provides regular model risk reporting to model oversight committees and the Board.

MAIN PURPOSE OF THE ROLE

Independent model validation of derivative pricing methodologies, both initial and periodic, across all asset classes and model types and in line with regulatory requirements and industry best practice. The validation regularly requires an independent implementation of the models and the implementation of alternative challenger models.

KEY RESPONSIBILITIES

  • Initial and periodic validation of pricing models
  • Designing, modelling and prototyping challenger models
  • Quantitative analysis and review of model frameworks, assumptions, data, and results
  • Testing models numerical implementations and reviewing documentations
  • Checking the adherence to governance requirements
  • Documentation of findings in validation reports, including raising recommendations for model improvements
  • Ensuring models are validated in line with regulatory requirements and industry best practice
  • Tracking remediation of validation recommendations

SKILLS AND EXPERIENCE

Experience :

Essential:

  • At least a first relevant experience in quantitative modelling (model development or validation) of pricing models

Optional:

  • Experience in any of other model types (AI models, Market risk models, Counterparty credit risk models, Capital models)

Competencies:

Essential:

  • Good background in Math and Probability theory - applied to finance.
  • Good knowledge of Data Science and Statistical inference techniques.
  • Good understanding of financial products.
  • Good programming level in Python or R or equivalent.
  • Good knowledge of simulation and numerical methods
  • Awareness of latest technical developments in financial mathematics, pricing, and risk modelling

Beneficial:

  • Experience with C++ or C# or equivalent

Optional:

  • Experience with AI models

Education :

  • A Postgraduate degree in a quantitative discipline (e.g., statistics, mathematics, mathematical finance, econometrics)

PERSONAL REQUIREMENTS

  • Strong problem solving skills
  • Strong numerical skills
  • A structured and logical approach to work
  • Excellent attention to detail
  • Excellent written and oral communication skills
  • Ability to clearly explain technical matters
  • A pro-active, motivated approach

We are open to considering flexible working requests in line with organisational requirements.

MUFG is committed to embracing diversity and building an inclusive culture where all employees are valued, respected and their opinions count. We support the principles of equality, diversity and inclusion in recruitment and employment, and oppose all forms of discrimination on the grounds of age, sex, gender, sexual orientation, disability, pregnancy and maternity, race, gender reassignment, religion or belief and marriage or civil partnership.

We make our recruitment decisions in a non-discriminatory manner in accordance with our commitment to identifying the right skills for the right role and our obligations under the law.

#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Get a Better Machine Learning Job After a Lay-Off or Redundancy

Redundancy in machine learning can feel especially frustrating when your role was technically advanced, strategically important, or AI-facing. But the UK still has strong demand for machine learning professionals across fintech, healthtech, retail, cybersecurity, autonomous systems, and generative AI. Whether you're a research-oriented ML engineer, production-focused MLOps developer, or applied scientist, this guide is designed to help you bounce back from redundancy and find a better opportunity that suits your goals.

Machine Learning Jobs Salary Calculator 2025: Figure Out Your True Worth in Seconds

Why last year’s pay survey is useless for UK ML professionals today Ask a Machine Learning Engineer wrangling transformer checkpoints, an MLOps Lead firefighting drift alarms, or a Research Scientist training diffusion models at 3 a.m.: “Am I earning what I deserve?” The honest answer changes monthly. A single OpenAI model drop doubles GPU demand, healthcare regulators release fresh explainability guidance, & a fintech unicorn pays six figures for vector‑search expertise. Each shock nudges salary bands. Any PDF salary guide printed in 2024 now looks like an outdated Jupyter notebook—missing the gen‑AI tsunami, the surge in edge inference, & the UK’s new Responsible‑AI framework. To give ML professionals an accurate benchmark, MachineLearningJobs.co.uk distilled a transparent, three‑factor formula that estimates a realistic 2025 salary in under a minute. Feed in your discipline, UK region, & seniority; you’ll receive a defensible figure—no stale averages, no guesswork. This article unpacks the formula, highlights the forces driving ML pay skyward, & offers five practical moves to boost your value inside the next ninety days.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.