Historical Data Management Quantitative Analysis Manager

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 team will partner with Risk Technology and Modelling teams in MCRA to design, implement, and optimize the market data management process using big data technology for data ingestion, data processing, data integration, data lake storage and data analytics.

The HDM Quantitative Analysis 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 the Historical Data Storage system including design of data quality control and enhancement logic.
• Develop and enhance quantitative methods for measuring and analysing the quality of historical market data used by various models across all Risk Modelling Analytics and Enterprise Scenario groups’ teams.

The Historical Data Management Quantitative Analysis Manager is responsible for the direction, coordination, implementation, control, and completion of the historic data management workflows while remaining aligned with the strategy, commitments, and goals of the MCRA group. Workflows include BAU work (data quality control and assurance, up to 1/3 of monthly time) as well as strategic projects: data migrations, new data onboarding, process improvement, system improvement and organizational change.

The Historical Data Management Quantitative Analysis Manager’s responsibilities include:

• Taking ownership of several workflows in the Historic Data Management team.
• Managing the work of team members involved in owned workflows and training of junior team members.
• Identifying and assigning project tasks based on the skill set, experience, and strengths of team members.
• Leading and supervising quantitative data analysis, including the preparation of statistical and non-statistical data exploration, data validation, and the identification of data quality issues.
• Coordinating the reporting of major data quality issues and the subsequent follow up with the recommended actions.
• Developing detailed project plans based on the current data system state reflected in data reports and making recommendations addressing business needs.
• Monitoring project implementation performance to ensure timely delivery and creating project status reports for internal team needs.
• Developing excellent leadership, internal customer relationships and communication skills to liaise effectively with all market data system stakeholders.
• Designing data solutions and analytics solutions and creating formal documentation for developed systems.
• Observing system reports and taking required actions, working with supporting Technology teams to address issues.
• Introducing process automation of data extraction and data pre-processing tasks.
• Coordinating ad-hoc data analyses to improve core processes, and the design and maintenance of complex data manipulation.
• Optimizing monitoring systems, documenting optimization solutions, and presenting results to non-technical audiences.
• Writing formal documentation using technical vocabulary.
• Conducting presentations to various stakeholders.

Qualifications:

• Educated to postgraduate level, with an excellent academic record in a quantitative field (e.g. econometrics, statistics, data science, computer science, quantitative finance, mathematics, etc.). Master or higher degree is strongly preferred.
• 10+ years of relevant working experience (technology or data science area) for Banking or Insurance companies or Consulting clients in these industries.
• Project management experience is a key requirement.
• Experience of work coordination of programmers / data scientists (especially in Python & SQL, knowing. R, Matlab, VBA, may help but is not essential),
• Experience of one or more of the following is an advantage: Project Management Certificates, Agile Methodology, Systems Design, Data Architecture, Project Reengineering, Risk and Finance Data Management.
• Keen interest in banking and finance, especially in the field of Risk Management.
• Proficiency in inventory control and process improvement.
• Consistently demonstrates clear and concise written and verbal communication skills.
• Self-motivated and detail oriented.
• Demonstrated project management and organizational skills and capability to handle multiple projects at one time.

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