Citi Group Market Risk Data Management Quantitative Analyst 

Poland, Masovian Voivodeship, Warsaw 

342497184

Historical Data Management (HDM) team within Market & Counterparty Risk Analytics (MCRA) is responsible for Data Governance, the Target State Operating model, and the Historical Data Storage system to provision financial markets, macroeconomic and consensus data for risk models’ usage across market risk, credit risk, treasury risk, scenario design and expansion processes to meet risk management requirements and regulatory expectations.

The HDM quantitative work stream will lead the effort to:

• Manage historical market factor time-series across all products and all regions related to Market Risk IMA and Counterparty Risk IMM models. This includes defining market data sources, collecting data, validating data, and developing data cleansing logics.
• Ensure that cleaned market factor data can meet regulatory requirements and can be used as the inputs for Citi’s IMA and IMM models.
• Define market data sources and manage Historical Data Storage system including design of data quality control and enhancement logic.
• Develop and enhance quantitative methods for measuring and analyzing quality of historical market data which are used by various models across all Risk Modelling Analytics and Enterprise Scenario groups’ teams.

The Market Risk Data Management Quantitative Analyst responsibilities include:

• Conducting quantitative data analysis, including preparation of statistical and non-statistical data exploration, data validation, and identification of data quality issues.
• Report major data quality issues and follow up with the recommended actions.
• Analyzing and interpreting data reports, making recommendations addressing business needs.
• Work with project management team to ensure timely delivery of the project.
• Creating formal documentation for developed system, observing system reports and take actions, work with supporting Technology teams to address issues.
• Optimizing monitoring systems, document optimization solutions, and present results to non-technical audiences; write formal documentation using technical vocabulary.
• Introducing process automation of data extraction and data pre-processing tasks, performing ad-hoc data analyses to improve the processes, design and maintain complex data manipulation processes, and provide documentation and presentations.
• Help in training of junior quantitative analysts.

Qualifications:

• Educated to postgraduate level, with an excellent academic record in a quantitative field (e.g. mathematics, physics, computer science, statistics, econometrics, quantitative finance, etc.). Master or higher degree is strongly preferred.
• 2+ years of relevant working experience, financial risk area is preferred.
• Programming experience with statistical analysis methods (team uses Python + SQL, knowing other languages, e.g. R, Matlab, VBA, may help but is not essential),
• Experience of one or more of the following is an advantage but not essential: risk management practices and procedures; numerical methods; Monte Carlo simulations; statistical hypotheses testing, derivative pricing and exotic products.
• Keen interest in banking and finance, especially in the field of Risk Management.
• Self-motivated and detail oriented.
• Demonstrated project management and organizational skills and capability to handle multiple projects at one time.

We offer:

• Work in a challenging area of the financial industry with one of the world's leading companies with exposure to variety of products, processes and controls.
• Cooperation with a high quality, international, multicultural and global team.
• Work in a friendly and diversified environment, appreciating differences in style and perspective and using them to add value to decisions leading to organizational success.
• Management supporting balanced and agile work (flexible working hours, home office).
• Attractive benefits package (Benefit System, medical care, pension plan etc.).
• A chance to make a difference with various affinity networks and charity initiatives.

Time Type:

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