Model/Anlys/Valid Officer Job at Citi (Tampa)

Job Responsibility

Apply quantitative and qualitative data analysis methods to extract, transform and analyze AML/KYC data using SAS, R programming, Python, and SQL
Perform validation of the bank’s KYC customer risk scoring tools and Anti-Money Laundering (AML) transaction monitoring tools to ensure regulatory compliance and establish risk and controls across multiple regions and lines of business, including Retail, Institutional, Credit Cards, Markets and Private Banking
Develop, enhance and validate quantitative methodologies for measuring and analyzing financial crimes risk – AML/KYC, ensuring the rule-based and machine learning (supervised and unsupervised) techniques are robust and effective
Serve the bank’s lines of defense by providing effective challenge to AML/KYC monitoring tools throughout their lifecycle, ensuring that conceptual soundness, technical specifications, data quality, and performance are validated in accordance with regulatory requirements
Evaluate theoretical construction and implementation of tools, challenging mathematical formulation, variables selection, threshold setting and code using statistical tests, ensuring assumptions and limitations are documented to assess fit-for-purpose
Data preparation and validation for modeling and analysis, handling of missing values and outliers, variable transformations, and feature pre-selection for tool stability
Automate data extraction and data preprocessing tasks, perform ad hoc data analyses, design and maintain complex data manipulation processes using Python
Establish robust controls around data quality, continuous monitoring of data quality flowing into AML tools
checks for completeness, anomalies, drifts, and integrity within core banking and data systems Mantas and EAP/Hadoop
Execute threshold analysis to evaluate and optimize production thresholds for AML monitoring scenarios, exploring data distributions and coverage analysis using SAS, R, Python or SQL to emulate and simulate tool logic
Conduct periodic reviews of the customer segmentation tool
validate AML monitoring tool’s risk tolerance by executing testing strategies, including risk-based sampling, sensitivity analysis, and random client analysis
Review performance monitoring of AML monitoring systems, establishing risk assessment and controls through KPIs and advanced statistical metrics to validate tool
Conduct data analysis, data mining – statistical and non-statistical data exploration, read and create statistical documentation, analyze and interpret data reports and work with risk management and technology teams to address data issues and improvements to AML tools and threshold settings
Communicate analytical findings, limitations, potential risks through presentations and reports (validation report, quarterly and annual review reports) to stakeholders, management and internal governance committees, and used to inform business decisions and ensure mitigating controls are in place
provide evidence-based responses to RFIs during regulatory examinations and internal audit reviews
Lead discussions on methodology and process improvement initiatives through adoption of automation and efficiency enhancements for automation of data inputs and tool performance validation
Maintain industry leading knowledge in global banking systems and operations, including financial products, client portfolios, market exposures, evolving regulatory requirements, risk and controls, and activities such as Correspondent Banking, Payment Intermediaries and cross-border transactions applicable for AML Detection and monitoring operations

Requirements

Master’s degree or foreign equivalent in Engineering (any), Data Science, Business Analytics, Mathematics, or related quantitative field
1 year of experience as a Data Analyst, Business Analyst, Banking Analyst, Officer, or related position involving AML and KYC risk and control procedures for banking services
Alternatively, will accept a Bachelor’s degree in the stated fields and 3 years of the specified experience
Fraud detection for money laundering/terrorist financing
Customer risk scoring and transaction monitoring rules and models for AML, KYC
Business Intelligence, Data Visualization for risk assessment, data reporting and KPI dashboards
Exploratory data analysis including empirical data distributions, data reconciliation, missing data imputation, and outlier analysis
Statistical analysis including sampling and supervised and unsupervised machine learning
Analytical programming using SQL,SAS, Python, Spark and Hadoop Ecosystem, VBA in Excel, XML

What we offer

medical, dental & vision coverage
401(k)
life, accident, and disability insurance
wellness programs
paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

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Model/Anlys/Valid Officer

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