Business Analytics Int Analyst– C11 – Internal Audit Analytics

The Digital Solutions and Innovation (DSI) team within Citi's Internal Audit Innovation function is seeking a Data Analytics Manager (C11) to join the Analytics Centre of Excellence (CoE) team. The CoE collaborates with Internal Audit stakeholders globally to identify opportunities, design, develop, and implement analytics that support audit activities, alongside automation initiatives that promote operational efficiencies and expand audit coverage.

The successful candidate will demonstrate deep proficiency in analytics technology, data engineering, and AI-driven tools, and will possess functional knowledge of banking processes, risk frameworks, and controls. They will be a key contributor to the organisation's forward-looking data strategy within audit — leveraging cutting-edge approaches such as agentic AI, large-scale data extraction, and advanced automation to drive meaningful audit outcomes.

Key Responsibilities:

• Participate in the innovative use of audit analytics across all phases of the audit lifecycle — planning, fieldwork, and reporting.
• Define data needs, design, and execute audit analytics in accordance with the Citi Internal Audit methodology and professional standards.
• Support the development and execution of automated routines to focus and enhance audit testing.
• Execute innovation solutions and pre-defined analytics in accordance with standard DSI-CoE procedures.
• Own the end-to-end execution of selected analytics activities, from data sourcing to final insight delivery.
• Assist audit teams in performing moderately complex audits across business areas including Consumer Banking, Investment Banking, Risk, Finance, Compliance, and Technology.
• Apply working knowledge of AI tools to automate data gathering, anomaly detection and audit workflow steps.
• Leverage large language models (LLMs) and AI orchestration frameworks to design intelligent, autonomous analytics pipelines that reduce manual effort and improve audit coverage.
• Contribute to the evaluation, piloting, and deployment of AI-driven audit tools within the DSI-CoE innovation agenda.
• Provide support to other members of the Analytics and Automation team, and the broader DSI team.
• Develop professional relationships with audit teams to assist in identifying analytics and automation opportunities.
• Build effective working relationships with technology and business teams in areas under audit, facilitating understanding of processes and data sourcing.
• Communicate analytics requirements, methodologies, and results clearly to both technical and non-technical stakeholders through strong verbal and written communication skills.
• Make practical recommendations for analytics enhancements that improve audit quality and efficiency.

• Promote continuous improvement in all aspects of audit automation activities — including the technical environment, software stack, and operating procedures.
• Stay current on emerging technologies, data platforms, and AI advancements relevant to audit analytics and innovation.

Key Qualifications and Competencies

• Minimum 6-8 years of experience as a business or audit analyst, providing analytical techniques and automated solutions to business needs.
• Demonstrated experience working in a global environment within a large, complex organization.
• Excellent technical, programming, and databases skills
• Excellent analytical ability to understand business processes and related risks and controls and develop innovative audit analytics based upon audit needs.
• Strong interpersonal and multicultural skills for interfacing with all levels of internal and external audit and management.
• Self-driven, problem-solving approach. Understanding of procedures and following these to keep quality and security of processes.
• Detail oriented approach, consistently performing diligent self-reviews of work product, and attention to data completeness and accuracy.
• Data literate, with the ability to understand and effectively communicate what data means to technical and non-technical stakeholders.

Proficiency in one or more scripting/programming languages:

• SQL — complex query writing, performance tuning, data extraction
• Python — data wrangling, statistical analysis, automation scripting, OCR
• Hadoop Ecosystem — Hive, PySpark, Apache Spark
• SAS
• Data Visualisation Tools(one or more preferred)--Tableau, MicroStrategy, Cognos
• No-Code / Low-Code Tools (one or more preferred)-KNIME & equivalent
• Process Automation Tools (one or more preferred)-Airflow / Xcepter
• Knowledge of AI tools to automate data gathering, anomaly detection and audit workflow steps.
• Leverage large language models (LLMs) and AI orchestration frameworks to design intelligent, autonomous analytics pipelines that reduce manual effort and improve audit coverage.
• Familiarity with applying AI/ML methodologies to audit and risk analytics use cases.
• Good to have working knowledge of enterprise data extraction platforms such as Dataflame, EAP.

Experience of the following areas would be a plus:

• Business Intelligence including statistics, data modelling, data mining, forecasting, and predictive analytics.
• Application of data science tools and techniques to advance insights from data interrogation.
• Working with non-structured data (e.g., PDF files, OCR techniques).
• Banking domain expertise across: Institutional Clients Group, Consumer, Corporate Functions, Markets, Services, Anti-Money Laundering, Regulatory Reporting.
• Big Data analysis using HUE, Hive, or equivalent tools.
• Project Management / Solution Development Life Cycle experience.
• Exposure to process mining tools: Celonis, UiPath, IBM PM.

Educational Requirements

• Masters's degree in Computer Science, Information Technology, Mathematics, Statistics, Finance, or a related quantitative field.