Data AI Solutions Sr Analyst - C12 - TAMPA

Overview of the Role

Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.

As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients' best interests. Our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.

As an Assistant Vice President Data Analyst (AVP C12) in the Enterprise Data Office Data Solutions team based in our Tampa office, you will be a key contributor to our Enterprise Risk Management strategy to leverage data as an asset. This role requires a strong, hands-on professional with proven experience in data analytics, particularly in the direct implementation of advanced AI/ML and Agentic AI solutions at an enterprise level. You will apply your expertise to develop, deploy, and optimize innovative data solutions, train team members, work independently on critical projects, and create impactful presentations that highlight key findings and recommendations. You will play a vital role in advancing our data governance and risk reporting capabilities within a dynamic global financial environment.

Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We'll enable growth and progress together.

Responsibilities

• Hands-on AI/ML & Agentic AI Solution Implementation:

  • Actively design, develop, and implement hands-on, enterprise-level AI/ML and Agentic AI solutions to enhance Enterprise Risk Reporting. This includes building and deploying models, developing intelligent automation, and integrating AI capabilities into existing data pipelines.

  • Directly contribute to the application of Agentic AI, LLM, and other Generative AI (GenAI) tools for creating innovative data analytical solutions, focusing on tangible, implementable outputs for Enterprise Risk Management.

  • Conduct rigorous testing, validation, and performance tuning of AI/ML models and solutions, ensuring their accuracy, reliability, and scalability in a production environment.

• Mentorship & Team Contribution:

  • Mentor junior data analysts, sharing best practices in AI/ML implementation, data analysis, data governance, and project management to foster their technical growth and enhance team capabilities.

  • Actively contribute to a culture of continuous learning and improvement within the team, promoting knowledge sharing and collaborative problem-solving.

• Independent Project Execution & Presentation:

  • Work independently to manage and execute complex data analysis and AI/ML implementation projects from inception to completion, demonstrating strong ownership and problem-solving skills.

  • Develop and deliver clear, concise, and standout presentations to internal stakeholders, summarizing technical findings, project progress, and actionable insights derived from AI/ML solutions.

• Core Data Analysis & Governance:

  • Perform in-depth system and data analysis to support Enterprise Risk Management data initiatives, ensuring strict alignment with Citi's data governance policies (CDGP) and standards (CDGS) framework.

  • Gather and document comprehensive Business Requirements Documents (BRDs) and Functional Requirements Documents (FRDs) for complex data-centric projects, including detailed specifications for data quality rules, data lineage, and data layer components, partnering closely with technology teams to ensure robust implementation.

  • Actively manage, update, and ensure timely resolution of project artifacts, tasks, issues, and action items within JIRA or similar project management tools, maintaining accurate tracking of progress and dependencies.

  • Contribute to the design, documentation, and maintenance of logical and physical data layers, including advanced data models, data dictionaries, and data lineage, to ensure robust, scalable, and efficient data solutions for Enterprise Risk Management.

  • Identify, analyze, and troubleshoot complex data quality gaps and issues, collaborating with business data owners and technology teams to propose and implement effective remediation strategies and monitoring routines.

  • Review and validate business requirements, data lineage, and supporting documentation for downstream risk reporting and analytics solutions, ensuring consistency, traceability, and governance alignment.

  • Contribute to the creation and maintenance of detailed data flow diagrams, process maps, and system documentation, ensuring accuracy and completeness.

  • Participate in data governance forums and support the implementation of reporting and tooling for the ERM Data Operating Model.

  • Assist with the tracking and remediation of RAID (Risks, Assumptions, Issues, Dependencies) log items and overall project progress against established plans.

  • Collaborate with business data owners to understand existing data processes and contribute to defining new, innovative requirements for end-to-end data solutions.

Qualifications

• Experience:

  • 5-7 years of progressive experience in data or systems analysis and solution delivery, with emphasis on data analytics, data quality initiatives, data governance, and system implementation within the financial services industry.

• Hands-on AI/ML & Agentic AI Expertise:

  • Demonstrated hands-on experience and advanced proficiency in designing, developing, and implementing AI/ML and Agentic AI solutions at an enterprise level. This includes practical experience with model development, deployment, and MLOps.

  • Strong working knowledge and practical application of Agentic AI, LLM, and GenAI tools for data analysis, automation, and solution development.

  • Expert-level proficiency in data analysis and management tools and applications, including Oracle, PL-SQL, advanced data lineage tools, and enterprise data cataloging solutions.

  • Familiarity with business intelligence and reporting concepts (e.g., dashboards, metrics, and reporting layers); experience reviewing documentation for BI tools such as Tableau is a significant plus.

• Independent Work & Mentorship:

  • Proven ability to work independently, taking full ownership of projects and initiatives from concept to delivery, with minimal supervision.

  • Experience mentoring junior team members, fostering skill development, and contributing to team-wide knowledge sharing.

• Domain Expertise:

  • Prior experience in the banking or financial services industry is required, with exposure to enterprise risk management or specific risk management categories, including an understanding of the role of data in risk reporting, monitoring, and decision-making.

• Soft Skills:

  • Exceptional analytical, problem-solving, and organizational skills, with the ability to interpret highly complex data requirements, synthesize diverse information, and translate them into actionable specifications.

  • Superior verbal and written communication skills, capable of articulating highly technical concepts clearly and persuasively to diverse audiences.

  • Advanced proficiency in Microsoft Excel, PowerPoint, and data flowcharting tools, with a proven ability to create visually engaging, data-driven, and highly informative presentations.

  • Demonstrated ability to communicate effectively, influence stakeholders, drive consensus, and navigate complex organizational structures in a dynamic, fast-paced environment.

• Education:

  • Bachelor's degree required in a quantitative field (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics); Master's degree preferred.