Machine Learning Data Operations Manager
Career GuideKey Responsibilities
- Own the end to end workflow for dataset creation, labeling, review, and release
- Define data quality standards and acceptance criteria for training and evaluation datasets
- Set up processes for sampling, auditing, and measuring labeling accuracy
- Manage labeling vendors and in house labeling teams, including performance and cost
- Create clear guidelines and playbooks for labelers and reviewers
- Partner with machine learning engineers to understand model requirements and translate them into data specs
- Track dataset health over time, including drift signals and coverage gaps
- Improve throughput by removing bottlenecks and standardizing handoffs
- Build reporting for quality, turnaround time, and cost per labeled item
- Ensure data governance practices, including access controls and documentation
- Coordinate incident response for data quality issues that impact model performance
- Support compliance needs for sensitive data handling and retention
Top Skills for Success
Stakeholder Management
People Leadership
Process Design
Program Management
Vendor Management
Quality Management
Data Literacy
Risk Management
Dataset Specification
Labeling Guideline Authoring
Labeling Quality Audits
Inter Annotator Agreement
Data Annotation Tooling
Sampling Strategy
Active Learning Operations
Model Evaluation Data Management
Data Governance
Privacy Compliance
Security Basics
Career Progression
Can Lead To
Data Operations Manager
Machine Learning Operations Manager
Data Quality Manager
Technical Program Manager
Transition Opportunities
Head of Data Operations
Director of Machine Learning Operations
Director of Data Governance
Machine Learning Product Manager
Responsible AI Program Lead
Common Skill Gaps
Often Missing Skills
Inter Annotator AgreementSampling StrategyLabeling Quality AuditsData GovernancePrivacy ComplianceCost ModelingIncident ManagementData Documentation
Development SuggestionsBuild a simple quality measurement system that includes sampling, audits, and clear pass fail thresholds. Practice writing labeling guidelines that are testable and easy to follow. Strengthen governance by learning core privacy concepts, access control patterns, and documentation habits. Improve business impact by tracking cost, turnaround time, and quality together and using them to prioritize process improvements.
Salary & Demand
Median Salary Range
Entry Level$110,000 to $145,000
Mid Level$145,000 to $190,000
Senior Level$190,000 to $260,000
Growth Trend
Growing demand. Companies scaling machine learning systems are investing more in data quality, labeling operations, and governance to improve model performance and reduce risk.Companies Hiring
Major Employers
GoogleAmazonMicrosoftAppleMetaNVIDIAOpenAITeslaUberDoorDashAirbnbNetflixStripeSalesforceServiceNowPalantirSamsungByteDanceShopifyAdobe
Industry Sectors
TechnologyEcommerceFinancial ServicesHealthcareAutomotiveMedia and EntertainmentLogisticsCybersecurityEnterprise SoftwareCustomer Support Platforms
Recommended Next Steps
1
Create a portfolio example of a labeling workflow including specs, guidelines, and an audit plan2
Learn one annotation platform well and document how you would run quality reviews in it3
Set up a lightweight metrics dashboard that tracks quality, throughput, and cost4
Draft a vendor scorecard and a weekly operating rhythm for vendor performance reviews5
Study common dataset risks such as leakage, bias, and drift and propose mitigation steps6
Partner with a machine learning engineer to translate model goals into clear data requirements7
Prepare interview stories that show measurable improvements in quality, speed, and cost