Quality Assurance & Analytics Specialist
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Job Title: Quality Assurance & Analytics Specialist
Department: Data Engineering / AI Data Services
Location: Global
Reports to: VP of AI Ops
Contract: Permanent
Travel Requirements: No
Salary: Open Budget
Job Summary / Overview
The Quality Assurance & Analytics Specialist establishes and maintains QA frameworks and
analytical processes for Digital Services and Products related projects. By auditing and reporting
on key quality metrics, this role drives continuous improvement efforts and ensures high-
quality data outputs. In collaboration with cross-functional teams, the specialist will also
develop and deliver training programs to enhance annotation accuracy, quality standards, and
best practices. Additionally, they implement feedback loops, provide ongoing coaching, and
leverage BI tools to deliver actionable insights that optimize overall performance and efficiency.
Key Responsibilities And Accountabilities
Quality Framework Development: Define and implement QA standards, guidelines, and
SOPs for data annotation projects.
Auditing & Reporting: Conduct regular audits to evaluate precision, recall, and
annotation accuracy; produce detailed dashboards using BI tools (e.g., Power BI,
Tableau).
Continuous Improvement: Identify process gaps and work with project managers and
data teams to institute corrective actions and optimize QA workflows.
Team Support & Training: Facilitate training sessions and give actionable feedback to
ensure consistent output quality.
Analytics & Insights: Analyze quality trends and help stakeholders make data-driven
decisions for resource allocation and process enhancements.
Documentation: Keep updated records of quality audits, corrective actions, and QA
processes for transparency and knowledge sharing.
Develop and deliver training programs to improve annotation accuracy, quality
standards, and best practices.
Collaborate with cross-functional teams to align training with evolving project needs and
best practices.
Conduct regular refresher sessions to reinforce quality expectations and address
common errors.
Create training materials such as documentation, guides, and video tutorials to support
continuous learning.
Main Job Requirements
Education and Specific Training
Bachelors degree in Data Analytics, Information Systems, or a related field.
Additional certification or coursework in quality assurance, data analytics, or BI reporting is
beneficial.
Work Experience
Minimum of 3 years of experience in quality assurance or data analytics, preferably in data
annotation, localization, or content moderation settings.
Proven experience working with BI and data visualization platforms (e.g., Power BI, Tableau).
Special Certifications (Non-Mandatory)
Certifications in Six Sigma, Lean, or other quality management methodologies are
advantageous.
Required Skills
Technical Skills
Proficiency in BI tools (Power BI, Tableau) for data visualization and KPI tracking.
Strong analytical ability and familiarity with SQL or similar querying languages.
Basic scripting (Python) for automating quality checks (preferred).
Understanding of QA frameworks, data governance, and continuous improvement practices.
Qualified Candidates should send CV to [email protected]
Department: Data Engineering / AI Data Services
Location: Global
Reports to: VP of AI Ops
Contract: Permanent
Travel Requirements: No
Salary: Open Budget
Job Summary / Overview
The Quality Assurance & Analytics Specialist establishes and maintains QA frameworks and
analytical processes for Digital Services and Products related projects. By auditing and reporting
on key quality metrics, this role drives continuous improvement efforts and ensures high-
quality data outputs. In collaboration with cross-functional teams, the specialist will also
develop and deliver training programs to enhance annotation accuracy, quality standards, and
best practices. Additionally, they implement feedback loops, provide ongoing coaching, and
leverage BI tools to deliver actionable insights that optimize overall performance and efficiency.
Key Responsibilities And Accountabilities
Quality Framework Development: Define and implement QA standards, guidelines, and
SOPs for data annotation projects.
Auditing & Reporting: Conduct regular audits to evaluate precision, recall, and
annotation accuracy; produce detailed dashboards using BI tools (e.g., Power BI,
Tableau).
Continuous Improvement: Identify process gaps and work with project managers and
data teams to institute corrective actions and optimize QA workflows.
Team Support & Training: Facilitate training sessions and give actionable feedback to
ensure consistent output quality.
Analytics & Insights: Analyze quality trends and help stakeholders make data-driven
decisions for resource allocation and process enhancements.
Documentation: Keep updated records of quality audits, corrective actions, and QA
processes for transparency and knowledge sharing.
Develop and deliver training programs to improve annotation accuracy, quality
standards, and best practices.
Collaborate with cross-functional teams to align training with evolving project needs and
best practices.
Conduct regular refresher sessions to reinforce quality expectations and address
common errors.
Create training materials such as documentation, guides, and video tutorials to support
continuous learning.
Main Job Requirements
Education and Specific Training
Bachelors degree in Data Analytics, Information Systems, or a related field.
Additional certification or coursework in quality assurance, data analytics, or BI reporting is
beneficial.
Work Experience
Minimum of 3 years of experience in quality assurance or data analytics, preferably in data
annotation, localization, or content moderation settings.
Proven experience working with BI and data visualization platforms (e.g., Power BI, Tableau).
Special Certifications (Non-Mandatory)
Certifications in Six Sigma, Lean, or other quality management methodologies are
advantageous.
Required Skills
Technical Skills
Proficiency in BI tools (Power BI, Tableau) for data visualization and KPI tracking.
Strong analytical ability and familiarity with SQL or similar querying languages.
Basic scripting (Python) for automating quality checks (preferred).
Understanding of QA frameworks, data governance, and continuous improvement practices.
Qualified Candidates should send CV to [email protected]
Job Title: Quality Assurance & Analytics Specialist
Department: Data Engineering / AI Data Services
Location: Global
Reports to: VP of AI Ops
Contract: Permanent
Travel Requirements: No
Salary: Open Budget
Job Summary / Overview
The Quality Assurance & Analytics Specialist establishes and maintains QA frameworks and
analytical processes for Digital Services and Products related projects. By auditing and reporting
on key quality metrics, this role drives continuous improvement efforts and ensures high-
quality data outputs. In collaboration with cross-functional teams, the specialist will also
develop and deliver training programs to enhance annotation accuracy, quality standards, and
best practices. Additionally, they implement feedback loops, provide ongoing coaching, and
leverage BI tools to deliver actionable insights that optimize overall performance and efficiency.
Key Responsibilities And Accountabilities
Quality Framework Development: Define and implement QA standards, guidelines, and
SOPs for data annotation projects.
Auditing & Reporting: Conduct regular audits to evaluate precision, recall, and
annotation accuracy; produce detailed dashboards using BI tools (e.g., Power BI,
Tableau).
Continuous Improvement: Identify process gaps and work with project managers and
data teams to institute corrective actions and optimize QA workflows.
Team Support & Training: Facilitate training sessions and give actionable feedback to
ensure consistent output quality.
Analytics & Insights: Analyze quality trends and help stakeholders make data-driven
decisions for resource allocation and process enhancements.
Documentation: Keep updated records of quality audits, corrective actions, and QA
processes for transparency and knowledge sharing.
Develop and deliver training programs to improve annotation accuracy, quality
standards, and best practices.
Collaborate with cross-functional teams to align training with evolving project needs and
best practices.
Conduct regular refresher sessions to reinforce quality expectations and address
common errors.
Create training materials such as documentation, guides, and video tutorials to support
continuous learning.
Main Job Requirements
Education and Specific Training
Bachelors degree in Data Analytics, Information Systems, or a related field.
Additional certification or coursework in quality assurance, data analytics, or BI reporting is
beneficial.
Work Experience
Minimum of 3 years of experience in quality assurance or data analytics, preferably in data
annotation, localization, or content moderation settings.
Proven experience working with BI and data visualization platforms (e.g., Power BI, Tableau).
Special Certifications (Non-Mandatory)
Certifications in Six Sigma, Lean, or other quality management methodologies are
advantageous.
Required Skills
Technical Skills
Proficiency in BI tools (Power BI, Tableau) for data visualization and KPI tracking.
Strong analytical ability and familiarity with SQL or similar querying languages.
Basic scripting (Python) for automating quality checks (preferred).
Understanding of QA frameworks, data governance, and continuous improvement practices.
Qualified Candidates should send CV to [email protected]
Department: Data Engineering / AI Data Services
Location: Global
Reports to: VP of AI Ops
Contract: Permanent
Travel Requirements: No
Salary: Open Budget
Job Summary / Overview
The Quality Assurance & Analytics Specialist establishes and maintains QA frameworks and
analytical processes for Digital Services and Products related projects. By auditing and reporting
on key quality metrics, this role drives continuous improvement efforts and ensures high-
quality data outputs. In collaboration with cross-functional teams, the specialist will also
develop and deliver training programs to enhance annotation accuracy, quality standards, and
best practices. Additionally, they implement feedback loops, provide ongoing coaching, and
leverage BI tools to deliver actionable insights that optimize overall performance and efficiency.
Key Responsibilities And Accountabilities
Quality Framework Development: Define and implement QA standards, guidelines, and
SOPs for data annotation projects.
Auditing & Reporting: Conduct regular audits to evaluate precision, recall, and
annotation accuracy; produce detailed dashboards using BI tools (e.g., Power BI,
Tableau).
Continuous Improvement: Identify process gaps and work with project managers and
data teams to institute corrective actions and optimize QA workflows.
Team Support & Training: Facilitate training sessions and give actionable feedback to
ensure consistent output quality.
Analytics & Insights: Analyze quality trends and help stakeholders make data-driven
decisions for resource allocation and process enhancements.
Documentation: Keep updated records of quality audits, corrective actions, and QA
processes for transparency and knowledge sharing.
Develop and deliver training programs to improve annotation accuracy, quality
standards, and best practices.
Collaborate with cross-functional teams to align training with evolving project needs and
best practices.
Conduct regular refresher sessions to reinforce quality expectations and address
common errors.
Create training materials such as documentation, guides, and video tutorials to support
continuous learning.
Main Job Requirements
Education and Specific Training
Bachelors degree in Data Analytics, Information Systems, or a related field.
Additional certification or coursework in quality assurance, data analytics, or BI reporting is
beneficial.
Work Experience
Minimum of 3 years of experience in quality assurance or data analytics, preferably in data
annotation, localization, or content moderation settings.
Proven experience working with BI and data visualization platforms (e.g., Power BI, Tableau).
Special Certifications (Non-Mandatory)
Certifications in Six Sigma, Lean, or other quality management methodologies are
advantageous.
Required Skills
Technical Skills
Proficiency in BI tools (Power BI, Tableau) for data visualization and KPI tracking.
Strong analytical ability and familiarity with SQL or similar querying languages.
Basic scripting (Python) for automating quality checks (preferred).
Understanding of QA frameworks, data governance, and continuous improvement practices.
Qualified Candidates should send CV to [email protected]