AML Independent Validation Analyst 2
Company Name:-
Citi
Job Location:-
Bengaluru, Karnataka
Job Summary:-
The Spec Analytics Analyst 2 is a developing professional role.
Applies specialty area knowledge in monitoring, assessing, analyzing and/or evaluating processes and data.
Identifies policy gaps and formulates policies.
Interprets data and makes recommendations.
Researches and interprets factual information.
Identifies inconsistencies in data or results, defines business issues and formulates recommendations on policies, procedures or practices.
Integrates established disciplinary knowledge within own specialty area with basic understanding of related industry practices.
Good understanding of how the team interacts with others in accomplishing the objectives of the area.
Develops working knowledge of industry practices and standards.
Limited but direct impact on the business through the quality of the tasks/services provided.
Impact of the job holder is restricted to own team.
Responsibilities:
Perform model independent validation of rule based statistical tools within the agreed schedule and ensure execution follows the global methodology and is in compliance with model governance policy, guidelines and OCC requirements.
Apply statistical methods to organize, analyze, and interpret data to perform model validations
Provide data pulls and reports directly from Oracle Database.
Write efficient SQL and PL-SQL queries in complex Very Large Scale database environment.
Monitor time and capacity used during query execution, design efficient query execution plans.
Provide technical support to the team by implementing automation tools and integrating MS Office Excel, Oracle Database and SAS platforms.
Design technical solutions supporting complex data reporting and analytical tasks.
Generate complex graphical data reports, including data crosstabs, scatter plots and statistical measures.
Ensure Data Quality and Reliability.
Design and implement various Data Quality methods in SAS and Oracle database.
Run quality checks against database and report all data issues.
Detect data anomalies and identify source of data problems.
Recommend data quality solutions.
Evaluate model assumptions, data completeness, limitations, conceptual soundness, functional soundness, modelling methodology, outcome analysis, etc.
of models being v
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