Data Quality Analyst
Odixcity Consulting
Job Overview
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Job Description
Job Summary
Odixcity Consulting is seeking a Data Quality Analyst to ensure the reliability, accuracy, and consistency of datasets crucial for Artificial Intelligence training and business intelligence systems. This role is fundamental in auditing datasets, identifying anomalies, resolving inconsistencies, and upholding high-quality data standards. You will be responsible for assessing dataset completeness, labeling accuracy, formatting consistency, and compliance with project guidelines, directly preventing model bias and enhancing AI prediction reliability.
Key Responsibilities
- Audit datasets for quality and consistency.
- Identify data errors and anomalies.
- Validate annotations and labeling accuracy.
- Develop data quality checks and validation rules.
- Monitor dataset integrity across projects.
Job Requirements
- Bachelor’s Degree in Data Science, Statistics, Computer Science, Information Systems, or a related field.
- Must possess strong data validation and auditing skills.
- Knowledge of data governance concepts is essential.
- Data Quality Management Certification (an advantage).
- Google Data Analytics Certification or Microsoft Data Fundamentals, Data Governance or Data Management Certification (optional).
- 3-5 years’ experience in data quality, analytics, or data operations.
Key skills/competency
- Data Quality
- Data Auditing
- Data Validation
- Data Governance
- Data Consistency
- Data Integrity
- AI Training Data
- Business Intelligence
- Anomaly Detection
- Statistical Analysis
How to Get Hired at Odixcity Consulting
- Research Odixcity Consulting's culture: Study their mission, values, and recent projects in data and AI on LinkedIn and Glassdoor.
- Tailor your resume: Customize your resume to highlight data quality, auditing, and AI data experience relevant to the role.
- Showcase certifications: Emphasize any Google Data Analytics or Microsoft Data Fundamentals certifications you possess.
- Prepare for technical questions: Practice explaining data quality frameworks, anomaly detection, and validation rules effectively.
- Demonstrate problem-solving: Be ready to discuss how you've resolved complex data inconsistencies in past roles.
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