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location iconLocations: Gdańsk
level iconLevel: specialist
time iconWorking hours: Full time

Data Scientist – Quantitative Risk Model (Remote Option in Poland)


Quantitative Analysis, Risk

Group Functions

Job Reference #



Gdańsk, Kraków, Warsaw, Wroclaw

Job Type

Full Time

Your role

Do you enjoy working with large data sets and are interested in quantitative modelling of risk data? Do you enjoy working in a highly specialized team to develop and deliver solutions? Then we are looking for you to:

• gain profound understanding of the business reality which is to be represented in a model
• develop methodologies to assess risks for UBS Group and different legal entities around the globe leveraging insight of data.
• analyze diverse portfolio data and risks, and build statistical models
• construct and analyze statistical loss distributions of underlying risk factors
• discuss model requirements and assumptions with stakeholders (e.g., validators, team, and risk control)
• test different possible model specifications and calibrations

Your team

You’ll be working in the Statistical Risk Aggregation Methodology team in Krakow, Poland or remotely from other parts in Poland. Our role is to develop, maintain, and apply UBS’ statistical risk aggregation framework for assessing the impact of simulated risk scenarios on the firm’s profitability and capital adequacy. The framework captures all risk types across all businesses. We develop and maintain a suite of scenario-aligned risk category models. You'll have exposure to a variety of portfolios and risk types (both position risks and consequential risks) as well as work in a diverse team.

Your expertise

• a Master's degree in a quantitative or data science related discipline (e.g. Economics, Econometrics, Finance, Financial Engineering, Mathematics, Physics, Statistics)
• sound knowledge of statistical and econometric methods and their applications
• general understanding and interest in banking and financial markets
• experience in quantitative modelling (of risk data) preferred.
• experience in handling large datasets
• organizational skills with the ability to work under pressure within tight deadlines
• team-orientated, while able to complete tasks independently
• an expert user of the R and/or Python, knowledge of functional programming and object-oriented programming is preferred

About us

UBS is the world’s largest and only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.

With more than 70,000 employees, we have a presence in all major financial centers in more than 50 countries. Do you want to be one of us?

Join us

At UBS, we embrace flexible ways of working when the role permits. We offer different working arrangements like part-time, job-sharing and hybrid (office and home) working. Our purpose-led culture and global infrastructure help us connect, collaborate, and work together in agile ways to meet all our business needs.

From gaining new experiences in different roles to acquiring fresh knowledge and skills, we know that great work is never done alone. We know that it's our people, with their unique backgrounds, skills, experience levels and interests, who drive our ongoing success. Together we’re more than ourselves. Ready to be part of #teamUBS and make an impact?

Disclaimer / Policy Statements

UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.

Please submit your application in English

You are kindly requested to include the following clause in your application: "Wyrażam zgodę na przetwarzanie moich danych osobowych zawartych w ofercie pracy dla potrzeb procesu rekrutacji zgodnie z ustawą z dnia 27.08.1997r. Dz. U. z 2002 r., Nr 101, poz. 923 ze zm."


  • iconAnalytics & Reporting, Banking
  • iconEnglish
  • iconGdańsk, Wrocław, Warszawa, Kraków, pomorskie, dolnośląskie, mazowieckie, małopolskie

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