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Wipro Limited in India seeks a Quality Lead to oversee data integrity in ML annotation workflows, shaping QA frameworks and driving continuous improvement across teams.
You will manage metrics like IRR, precision, recall, lead calibration sessions with policy leads and engineering, and ensure high-quality outputs before model training.
This role emphasizes data-driven problem solving, cross-functional collaboration, and adherence to strict quality standards.
Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com.
Job Description:
Role Purpose
The purpose of this role is to increase revenue, maximize process efficiency & cost-effectiveness, and ensure excellent customer experience, through effective supervision of daily operations and personnel, contract compliance, resource optimization and capability development within an account.
1. Statistical Analysis and Data-Driven Problem Solving:
Demonstrated ability to analyze large volumes of operational data to identify defect trends, calculate key performance metrics (such as Inter-Rater Reliability, precision, and recall), and make evidence-based decisions to improve overall dataset health.
2. Root Cause Analysis (RCA) Methodology:
Proven expertise in systematically investigating quality regressions. The candidate must be able to trace errors back to their origin—whether stemming from tool limitations, policy ambiguity, or human error—and implement effective Corrective and Preventive Actions (CAPA).
3. Cross-Functional Communication and Alignment:
Strong verbal and written communication skills required to bridge the gap between technical and non-technical teams. The candidate must be capable of presenting complex quality reports to engineering stakeholders while providing clear, actionable feedback to operational workforces.
4. Quality Framework Design and Standardization:
Experience developing, implementing, and maintaining rigorous audit frameworks and Standard Operating Procedures (SOPs). The ability to define statistically significant sampling strategies that balance rigorous quality checks with operational throughput.
5. Team Calibration and Performance Coaching:
The ability to lead regular calibration sessions to align multi-tiered teams on subjective guidelines. Must possess the coaching skills necessary to design structured feedback loops that effectively correct annotator behavior and reduce recurring defect rates.
Responsibilities
The Quality Lead is responsible for defining, measuring, and upholding the data integrity standards for machine learning annotation workflows. This role involves designing robust quality assurance (QA) frameworks, managing team performance metrics, and driving continuous improvement initiatives. The ideal candidate will act as the final gatekeeper for data accuracy, ensuring that all deliverables meet strict engineering thresholds before model training.
Key Competencies and Responsibilities:
Quality Assurance & Audit Frameworks:
Design and manage statistical sampling strategies to evaluate large-scale annotation datasets. Oversee the daily operations of QA auditors to ensure rigorous, unbiased reviews of ground-truth data.
Metrics Tracking & Reporting:
Monitor, analyze, and report on primary quality metrics, including Inter-Rater Reliability (IRR), precision, recall, and overall defect rates. Develop and maintain dashboards to provide ongoing visibility to cross-functional stakeholders.
Root Cause Analysis (RCA):
Lead deep-dive investigations into quality regressions or sudden drops in annotator performance. Identify whether errors stem from tooling defects, ambiguous policy documentation, or operational oversight, and implement corrective actions.
Feedback Loops & Calibration:
Establish structured, actionable feedback mechanisms for the annotation workforce to correct behavioral trends and reduce recurring errors. Lead regular calibration sessions with Policy Leads and Engineering to align on quality definitions and subjective edge cases.
Process Optimization:
Identify bottlenecks in the QA workflow and collaborate with operational managers to streamline auditing processes, ensuring high-quality output without severely impacting overall throughput.
Core Qualifications:
Proven experience in quality management, data operations, or a highly analytical QA role.
Strong proficiency in statistical analysis, data sampling methodologies, and identifying defect trends.
Detail-oriented mindset with the ability to communicate complex quality issues clearly to both technical and non-technical teams.
Good to have Hiring Skills
Practical experience with, or formal certification in, continuous improvement methodologies such as Lean, Six Sigma (Green Belt or above), or Kaizen,
Prior exposure to industry-standard data annotation platforms and an understanding of how tool design, hotkeys, and user interface ergonomics directly impact annotator fatigue and error rates.
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Wipro is committed to creating an accessible, supportive, and inclusive workplace. Reasonable accommodation will be provided to all applicants including persons with disabilities, throughout the recruitment and selection process. Accommodations must be communicated in advance of the application, where possible, and will be reviewed on an individual basis. Wipro provides equal opportunities to all and values diversity.
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