Citi is looking to leverage advanced analytics and data science to improve enterprise-wide data quality and integrity, by rigorously assessing data quality rules, identifying patterns, and predicting downstream implications to enable precise and effective decision-making.
Requirements
- 10+ years of experience in data science, data engineering, analytics, or a related field within the financial services industry.
- Proven experience managing complex data projects, particularly in financial data rule enforcement and exception management.
- Strong understanding of financial data structures, regulations, and market mechanics.
- Deep expertise in programming languages required (examples: Python, R, SQL, Java, Ruby, Rust).
- Strong statistical math skills required (examples: linear algebra, calculus and statistics are needed for Time Series Forecasting).
- Expertise in programming applications (e.g., Python, R, SQL), and databases (e.g. Hadoop)
- Skilled with data governance solutions (e.g., Collibra, Alation, Manta, Ab Initio, etc.) and financial software (e.g., Bloomberg, Reuters).
Responsibilities
- Drive the implementation of advanced statistical and analytical methods, including machine learning and predictive modeling, to detect anomalies, and uncover underlying patters.
- Leverage data profiling and predictive modeling to forecast trends, rules refinement, and risk factors that could potentially impact the business
- Support the development, implementation, and continuous refinement of data quality rules specific to finance datasets, ensuring alignment with regulatory standards such as GDPR, SOX, and other applicable compliance frameworks.
- Develop and deliver reporting to support detailed analysis of finance data quality to support identification of issues, root causes, and actionable remediation strategies.
- Establish robust reporting and dashboards to track, trend, and communicate data quality and data quality process metrics, highlighting alignment to SLAs, alignment to data quality thresholds, and areas for improvement.
- Provide reporting and analytics to support data quality validation and cleansing processes to minimize data discrepancies and ensure accuracy in financial reporting.
- Develop and implement frameworks to identify, analyze, and resolve exceptions and breaks in data systems, such as reconciliation discrepancies, transactional inconsistencies, or reporting anomalies.
Other
- The role requires strong analytical, interpersonal and leadership skills that rely on influence and building effective partnerships to execute on our delivery plans and controls in a systematic, measurable, and effective way.
- Leadership experience with the ability to manage cross-functional teams and deliver complex projects.
- Strong strategic thinking and decision-making skills.
- Excellent problem-solving ability, particularly in high-pressure financial environments.
- Exceptional communication skills, with the ability to present complex analytical insights to senior executives and non-technical stakeholders.
