Senior QA/QC Data Analyst with AI/ML testing

NTT
Bengaluru
Workplace: OnsiteFull timeFunction: Data Analytics & Business IntelligenceEducation: bachelorsSkills: ["Ownership","Problem-solving","Root cause analysis","Communication","Stakeholder management"]

Own and execute QA/QC for AI/ML data and model quality, from evaluating dataset integrity to validating accuracy, precision, recall, and stability against thresholds. Build automated data validation, model regression, and pipeline checks within CI/CD and MLOps workflows. Identify bias and drift, track and remediate data/AI defects with root-cause analysis, and ensure governance/privacy/security and regulatory adherence. Communicate quality status and risks to stakeholders before and after releases.

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FursaFursa
NTT
NTT
1 month ago

Senior QA/QC Data Analyst with AI/ML testing

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 17 hours agoStatus: Live

Job Summary

Own and execute QA/QC for AI/ML data and model quality, from evaluating dataset integrity to validating accuracy, precision, recall, and stability against thresholds. Build automated data validation, model regression, and pipeline checks within CI/CD and MLOps workflows. Identify bias and drift, track and remediate data/AI defects with root-cause analysis, and ensure governance/privacy/security and regulatory adherence. Communicate quality status and risks to stakeholders before and after releases.
Location: Bengaluru
Workplace: Onsite
Employment Type: Full time
Job Function: Data Analytics & Business Intelligence
Seniority: Mid level

Key Responsibilities

  • •Evaluate dataset quality across accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity, and define quality criteria for AI model performance.
  • •Test data ingestion, ETL/ELT transformations, and outputs to ensure source-to-target integrity and validate that AI/ML datasets are accurate, representative, unbiased, and fit for purpose.
  • •Validate model metrics (accuracy, precision, recall, stability) against business/technical thresholds; detect and assess bias, fairness issues, and unintended impacts.
  • •Implement automated tests for data validation, model regression, and pipeline checks within CI/CD and MLOps workflows, including model-centric regression testing and drift detection.
  • •Log, prioritize, and track data/AI defects; perform root cause analysis and corrective actions; monitor data/model drift and communicate quality status, risks, and recommendations to stakeholders.

Key Requirements

  • •5+ years of related work experience, plus a Bachelor’s degree in computer science, information systems/technology, software engineering, or a related discipline (or equivalent experience).
  • •Experience designing and executing data quality criteria for accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity.
  • •Proficiency with PyTest and automated testing for ML/data validation, including model regression and pipeline checks in CI/CD and MLOps.
  • •Hands-on knowledge of AI/ML testing concepts such as inference endpoint API testing, model metrics validation, drift monitoring, and bias/fairness assessment.
  • •Knowledge of CI/CD integration and release/defect tracking tools (e.g., Azure pipelines, Jenkins, Zephyr) and strong communication with technical/non-technical stakeholders.
Experience:AI/MLData qualityMLOps
Education:Bachelor's in computer science, information systems, information technology, software engineering, computer engineering, or a related discipline
Skills:OwnershipProblem-solvingRoot cause analysisCommunicationStakeholder management
Certifications:ISTQB
Tech Stack:PyTestPythonC#RGreat ExpectationsETLELTCI/CDMLOpsAzure CICD PipelineJenkinsCruise ControlGitGitHubSVNTFSDevOpsZephyrFiddlerPostman

Company Brief

NTT
Dimension Data, operating under NTT Ltd, provides managed IT services, cloud and data center solutions, networking, cybersecurity, and digital transformation services to enterprise customers worldwide.
Industry: Professional Services
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Valuation: Public Company (Market Cap in USD)
Headquarters: London, United Kingdom
Founded: 1983
WebsiteLinkedIn