Quantitative Assistant Vice President

Mitsubishi UFJ Financial Group
London
1 day ago
Create job alert
Overview

Risk Analytics Group (RAG) is a specialised area within the Risk Department, responsible for Market Risk Models, Capital Models, Counterparty Exposure Models and Risk models in general. The team members have strong quantitative skills and the team head reports to the local and international Chief Risk Officer. This role is part of the Portfolio Credit Analytics sub‑team of RAG. The team is responsible for the development and maintenance of economic capital models, portfolio credit risk models, scenario based stress testing models, product specific stress testing models, and rating models. The candidate will participate in the development and maintenance of economic capital models, stress expected loss models, and other stress testing models. The candidate will work with other colleagues in the team, in the wider Risk Analytics Group, and in the Risk area, in particular credit risk managers and model validation.


Responsibilities

  • Developing, maintaining and improving economic capital models and other models the team is responsible for such as ECL stress testing.
  • Designing and running model validation tests, for both model assumptions and implementation.
  • Investigating issues and proposing changes where there are model weaknesses.
  • Specifying and testing system changes to implement improvements.
  • Ad-hoc projects as required, including collaboration with business, Credit Risk Management and Model Validation.
  • Investigating issues relating to the Credit Risk Models.
  • The role holder will be assessed in accordance with their employing entity's performance framework and process with relevant input obtained from the dual hatting entity as relevant.
  • As duties and responsibilities change, the job description will be reviewed and emended in consultation with the role holder.
  • The role holder will carry out other duties as are within the scope, spirit and purpose of the role as requested by their line manager or Department Head.
  • The role holder is required to follow regulatory requirements applicable to ensure each business is appropriately supported and to maintain the legal entity integrity of each of MUFG Bank and MUS.
  • Working terms are dictated by functional mandates, the terms of the Dual‑Hat Arrangement Agreement in place between MUFG Bank and MUFG Securities EMEA plc and any other relevant agreements entered into between MUFG Bank and MUFG Securities EMEA plc.
  • The role holder will have responsibility for identifying and resolving where there may be a difference or conflict in needs between MUFG Bank and MUFG Securities EMEA plc, escalating to their manager where required.
  • We are open to considering flexible working requests in line with organisational requirements.

Requirements

  • Previous experience with statistical models in finance

Desirable

  • Previous experience in Economic Capital models, including PD, LGD and EAD modelling
  • Previous experience in Portfolio Credit Risk modelling

Work Experience

  • 2 to 5 years experience in a Financial Services firm

Skills and Experience

  • Strong knowledge in statistics
  • Knowledge of advanced programming languages (Python)
  • Portfolio Credit Risk modelling
  • Knowledge of basic theory of default modelling, Highly numerate education (Maths, Statistics, Engineering, Computer Science) at MSc level or above, Excellent communication skills with the ability to adjust to different audiences
  • Highly motivated and innovative, able to work on own initiative
  • Excellent accuracy and attention to detail with an analytical mind‑set
  • Good team player with professional attitude
  • Good time management and ability to prioritise
  • Ability to manage large workloads and tight deadlines, balancing urgent tasks and longer term projects
  • Strong decision making skills, the ability to demonstrate sound judgement
  • Strong problem solving skillsStrong numerical skills

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world's leading financial groups. Across the globe, we're 150,000 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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Financial Crime Screening - Data Scientist

Financial Crime Screening - Data Scientist

Risk - Quantitative Engineering - Vice President - London

Asset & Wealth Management - Quantitative Strategist, XIG - Vice President - London

Global Banking & Markets, FICC SMM Quantitative Researcher, Associate / VP, London

Quantitative Survey Analyst – Large-Scale Health Data

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 Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.