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Salary
$37k – $86k per year (Estimated)
Location
In office (Amsterdam)
Seniority
Junior · 2+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
ING is a Dutch banking group formed in 1991 by the merger of the postal bank NMB Postbank and the insurer Nationale-Nederlanden, and it was among the first large banks to build a direct, branch-light retail model. It serves retail customers in the Netherlands, Belgium, Germany, Poland, Spain and several other markets through mobile-first banking, and runs a substantial wholesale bank covering lending, transaction services and financial markets for corporate clients. Headquartered in Amsterdam and listed on Euronext, the group divested its insurance operations after the financial crisis to focus on banking.

ING NL is looking for a Quantitative Model Risk Specialist to strengthen the Predictive Analytics team within the Integrated Risk Department (IR).

This position is suited for a professional with a strong quantitative foundation who is eager to further develop within credit risk modelling.

We are looking for someone with a solid analytical background and initial experience in IRB/IFRS9 rating models and/or Credit Decision Models (e.g. scorecards, Early Warning Systems), and an interest in the Model Lifecycle and emerging topics such as AI and advanced analytics.

The team

Predictive Analytics is responsible for the (co-)development and management of regulatory and non-regulatory Credit Risk models with state-of-the-art modelling methods, tooling, and data processing technologies. These models are core to the success of ING and they are applied for different purposes, amongst others to determine capital adequacy, loan loss provisions but also credit decisions and in-life & problem management of loans.

You will work in an Agile environment, collaborating with colleagues across Risk, Finance, Business, and IT. The role offers strong opportunities to develop your modelling, data, and AI-related skills in a practical setting.

Roles and responsibilities

The core task is to make an analytical contribution in maintaining a healthy lending portfolio in the near and far future. Your role will be to:

  • Developing and maintaining models for measuring and managing credit risk for Dutch Portfolio.

  • Model development of regulatory models for IRB/IFRS9 purposes.

  • Forecasting and describing developments in provisions, risk costs, RWA and arrears are important components.

  • Model development for credit decision models, in life management models (EWS etc.)

  • Delivery of bank wide ESG strategy and supporting managing and analysing ESG risk.

  • Supporting ING Bank Netherlands new products, processes via measuring credit risk

  • adequately and support decision making.

  • Collaborating with Risk managers within the department to develop and validate an adequate credit risk policy.

  • Collaborating with the front office, as well as the ING Group Risk and Finance departments, to align the various interests and to exchange knowledge.

  • Collaborating with IT system owners, to ensure adequate data/platform management.

  • Contribute to the exploration and application of AI / machine learning techniques in credit risk modelling where applicable

  • Use modern analytics tools (e.g. Python-based libraries) to improve modelling and analysis workflows

  • Stay up to date with emerging trends in AI and data science and actively apply learnings in day-to-day work

  • Work according to ING’s one Way of Working (agile WoW)

How to succeed

We hire people for their potential. In this role, we expect curiosity, ownership in your scope, and a willingness to continuously develop your expertise.

  • 2-5 years of experience in Credit Risk Modelling, including relevant experience in IRB/IFRS9 modelling and/or Credit Decision modelling.

  • Deep knowledge of quantitative methods and techniques, experience with Data Science and Machine Learning combined with business knowledge of Credit Risks.

  • MSc degree or PhD in e.g. mathematics, physics, econometrics

  • Experience with development of (credit) (risk) models

  • Excellent knowledge of statistics and/or mathematics

  • Excellent knowledge of programming, preferably in SAS Base, SAS Macro Language, SQL, VBA and Python

  • Experience with risk modelling and business tooling, specifically SAS EG, MS Access, MS Excel, SharePoint

  • Experience with (central) data gathering and processing

  • Knowledge of banking and financial industry, financial and lending products, and processes

  • Interest in or exposure to machine learning / AI applications in risk or analytics

  • Experience in being a sparring partner/advisor to senior management

  • You have strong analytical and problem-solving execution skills

  • Excellent communication skills writing and reporting in English.

Rewards and benefits

We want to make sure that it’s possible for you to strike the right balance between your career and your private life. Find out more about our employment conditions.

The benefits of working with us at ING include:

  • A salary tailored to your qualities and experience

  • Great international career opportunities

  • Flexible working hours and the possibility to work at home

  • 25-28 vacation days depending on contract

  • Pension scheme

  • 13th month salary

  • Individual Savings Contribution (BIS), 3.5% of your gross annual salary

  • 8% Holiday payment

  • Personal growth and challenging work with endless possibilities to realize your ambitions

  • An informal working environment with innovative colleagues who strive for the very best

  • Progressive way of working according to the Agile method, so that new ideas come to life

About us

Curious about how ING empowers people and businesses to move forward?

Discover what we do and what we can offer you.

Questions?

Please visit our Frequently Asked Questions section to find some answers on questions you might have.

Contact the recruiter attached to the advertisement. Want to apply directly? Please upload your CV and motivation letter by clicking the ‘Apply’ button.

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