Machine Learning Data Operations Manager

Career Guide

A Machine Learning Data Operations Manager leads the day to day work that turns raw data into reliable training and evaluation data for machine learning systems. The role combines people leadership, process design, data quality management, and close partnership with engineering and product teams to deliver consistent, auditable, and scalable data operations.

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Key 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

Create a portfolio example of a labeling workflow including specs, guidelines, and an audit plan

Learn one annotation platform well and document how you would run quality reviews in it

Set up a lightweight metrics dashboard that tracks quality, throughput, and cost

Draft a vendor scorecard and a weekly operating rhythm for vendor performance reviews

Study common dataset risks such as leakage, bias, and drift and propose mitigation steps

Partner with a machine learning engineer to translate model goals into clear data requirements

Prepare interview stories that show measurable improvements in quality, speed, and cost