Data Quality Analyst

• Proactive data-quality checks:
  Establish checks that identify problematic data before it enters or impacts AML models.
• Data profiling:
  Profile datasets to understand data characteristics, quality issues, anomalies, and potential problems.
• Data issue / defect identification:
  Detect data defects or quality issues affecting the datasets used by AML models.
• Data issue management and resolution:
  Track, investigate, and help remediate identified data-quality issues rather than simply reporting them.
• Model-input quality assurance:
  Provide confidence that the data being supplied to AML models meets expected quality standards before it is consumed by the models.
• Preventive controls / monitoring:
  Move the process from reactive detection to preventive monitoring so issues are caught earlier in the data lifecycle.

• Demonstrated accountability for analytical or quantitative models across implementation, maintenance, and controlled change processes.
• Experience operating within governed environments requiring formal documentation, controls, and audit readiness.
• Ability to independently challenge model assumptions and interpret outputs without serving as primary model developer.
• Proficient in performing data analysis and validation using SQL in Teradata environments.
• Ability to analyze large datasets to identify trends, issues, and insights supporting business decisions.
• Experience monitoring, validating, and reporting on data quality metrics including accuracy, completeness, and timeliness.
• Strong capability to conduct root cause analysis for data quality issues.
• Demonstrated experience partnering with stakeholders to drive resolution of data defects and control gaps.
• Proven experience managing complex projects with multiple stakeholders across technology, data, and business teams.
• Ability to coordinate timelines, deliverables, dependencies, and risk issues to successful completion.

“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”