Required Skills: QA, UAT, Python, Banking, AML, KYC
Job Description
Required Skills:
· Proficiency in Python coding (2-5+ years of experience)
· Experience with risk analysis and decision-making
· Ability to write clear and precise user requirements for business metrics, datasets, and dashboards
· Ability to interact confidently with senior management
· Experience in performing QA and UAT
· At least 1+ year of banking experience
Preferred Skills:
· Advanced Excel skills (VLookups & Pivot Tables)
· Familiarity with AML/KYC and CDD
· Experience with Jira and SharePoint
Soft Skill Fit:
· Ability to be coachable, self-aware, and adaptable
· Excellent communication skills, both written and verbal
· Strong sense of intuition and common sense
· Comfortable with presenting data and findings
Position Summary:
We are seeking a detail-oriented Financial Crimes QA Analyst to join our team for a Trade Operations QA project within the Transactional Risk Intelligence group. This role involves conducting quality reviews and data checks to ensure the accuracy and integrity of financial crime-related information. The ideal candidate will have a blend of Python coding expertise and data validation skills, with a strong understanding of banking operations.
Key Responsibilities:
· Python Coding & QA: Perform QA on Python code, conduct data validation, and edit existing scripts. No prior QA experience needed as training will be provided.
· Data Comparison & Analysis: Compare data from SharePoint to scripts and system records, ensuring consistency and accuracy.
· Transactional Risk Review: Evaluate transaction reports, customer data, and sensitive information to identify red flags and perform due diligence.
· Documentation & Reporting: Produce monthly reports using Tableau and PowerPoint, and present findings to business partners.
· Communication & Collaboration: Interact effectively with senior management, team members, and stakeholders. Set up meetings and manage communications via Outlook and Teams.
· Training & Presentations: Develop and deliver training materials for the QA group and present data findings to the team.