Experienced Quantitative/Data Analyst

ESM - European Stability Mechanism

Luxembourg

Job Description

We are looking for an experienced quantitative/data analyst for the Derivatives and Market Intelligence team within the Funding and Investor Relations division to provide insights on financial market developments. The selected candidate will be responsible for the entire process of transforming raw data into actionable insights, and will report to the Head of Funding and Investor Relations.

The Funding and Investor Relations division executes all funding activities for the European Stability Mechanism (ESM) and the European Financial Stability Facility (EFSF), manages relationships with a global investor base, manages the derivatives portfolio related to funding transactions, and analyses the market from a quantitative and qualitative perspective. The division also works with debt management offices and in the sovereign, supranational, and agency issuer community.

KEY ACCOUNTABILITIES

Conduct independent research using large data sets;
Develop statistical tools and predictive models to support the division’s analytical work;
Implement and maintain robust data analysis tools and algorithms by using Python and relevant database management systems;
Collect, clean, and prepare data for analysis;
Analyse financial data for trends and correlations;
Organise, interpret, and visualise data;
Conduct extensive data mining to identify trends in the fixed-income market;
Conduct targeted ad hoc quantitative analysis on topics relevant to the division;
Either at least two years of relevant work experience in quantitative analysis related to financial markets or applicable to time series causal relationships, statistical modelling, and inference, or, for PhD holders, up to two years of relevant research work performed in the context of PhD studies.

KEY BACKGROUND & EXPERIENCE

PhD or master’s degree, preferably in data science, finance, statistics, economics, mathematics, physics, or another relevant field;
Experience in financial services is an advantage;
Experience in one or several of the following areas of expertise is a significant advantage:
- data visualisation in Excel and Python;
- programming experience in Python & VBA;
- causal inference;
- statistical analysis of large data sets;
- SQL scripting.
Proven knowledge of relational databases, including work with MySQL or SQL Server is an advantage;
Familiarity with a range of the standard databases providing financial data (Bloomberg, Refinitiv) is an advantage;
Fluency in written and spoken English.

KEY COMPETENCIES

Ability to take ownership and work both independently and collaborate within a small team;
Analytical and problem-solving skills;
Critical and innovative thinking;
Communication, presentation and interpersonal skills;
Resilience, flexibility, and adaptability to changes.

PRACTICAL/ADDITIONAL INFORMATION

Deadline for application: 7 November 2024;
Employment grade: Officer/Senior Officer;
Contract duration: two-year fixed-term contract;
Place of employment: Luxembourg;
Current working arrangements: possibility to work remotely, usually up to two days a week, from Luxembourg and the border region (or from further away under certain conditions).

The ESM aims to provide equal opportunity to all candidates irrespective of their gender, nationality, age, race, culture, education, religious beliefs, sexual orientation or disability. To further enhance the diversity of our workforce, we particularly encourage women to apply.

Note 1: The ESM reserves the right to refrain from hiring, or to slightly modify job responsibilities, as needed.

Note 2: As a result of the selection process, please note that you may be placed on a ‘reserve list’ valid for a duration of six months during which you might be considered for a similar position with a similar level of requirements. Only those candidates who have been notified will be placed on the ‘reserve list’. After this period, you will need to apply again for our vacancies to be considered for a different selection process.

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