Technology Lead Business Analyst - Vice President

Citi is seeking a highly experienced Business and Data Analysis Lead to play a pivotal role in the firm-wide implementation of the Fundamental Review of the Trading Book (FRTB) Internal Model Approach (IMA). This is a leadership position for a subject matter expert who will bridge the gap between complex regulatory requirements, quantitative modeling, and technology execution.

This individual will be the central authority on business requirements and data analysis for our most critical risk calculations, ensuring that the sophisticated models for Expected Shortfall, Non-modelable Risk Factors, and Historical VaR are built on a foundation of complete, accurate, and well-understood data, perfectly aligning with regulatory intent. This new delivery of FRTB IMA is a significant regulatory initiative requiring specialized expertise to translate complex business requirements into robust technical solutions.

Responsibilities:

As the lead Business Data Analysis expert, this individual will be the primary driver for defining the "what" and "why" behind our risk calculation systems. They will immerse themselves in the intricacies of FRTB IMA, Internal Risk Management VaR, and Stress Testing methodologies, translating them into precise specifications that our quantitative and technology partners will build upon. Their expertise will ensure the integrity and accuracy of the entire risk calculation framework. By leading the analysis and defining the data strategy, they will be directly responsible for the conceptual soundness of the solutions, guaranteeing they are not just computationally efficient but also robust, defensible, and fully compliant with regulatory standards.

Key Responsibilities include:

• Acting as the lead Subject Matter Expert (SME) for Market Risk regulations, primarily FRTB IMA, translating dense regulatory text into clear business and data requirements.

• Authoring detailed business and functional requirement documents, specifying the logic for risk calculations, data transformations, and reporting.

• Defining the end-to-end data strategy, including sourcing, lineage, quality controls, and validation rules for all required market and trade data.

• Performing deep-dive data analysis using SQL, Python (e.g., Pandas), or other tools to identify data gaps, resolve inconsistencies, and ensure data integrity.