Data Labeling Analyst Ii information
See Remote, OR salary details
$43.2K - $52.5K
7% of jobs
$52.5K - $61.8K
13% of jobs
$62.3K is the 25th percentile. Wages below this are outliers.
$61.8K - $71K
18% of jobs
The median wage is $75.3K / yr.
$71K - $80.3K
16% of jobs
$80.3K - $89.5K
13% of jobs
$93K is the 75th percentile. Wages above this are outliers.
$89.5K - $98.8K
9% of jobs
$98.8K - $108.1K
5% of jobs
$108.1K - $117.3K
9% of jobs
$117.3K - $126.6K
3% of jobs
$126.6K - $135.9K
2% of jobs
How much do data labeling analyst ii jobs pay per year?
As of Aug 23, 2026, the average yearly pay for data labeling analyst ii in Remote, OR is $82,559.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,400.00 and $96,900.00 per year, depending on experience, location, and employer.
A Data Labeling Analyst II is responsible for accurately categorizing and annotating large datasets, which are often used to train machine learning models. This role involves reviewing data such as images, audio, text, or videos and applying predefined tags or labels according to specific guidelines. A Data Labeling Analyst II may also help improve labeling processes, provide feedback on data quality, and support junior analysts. Their work ensures that AI and data-driven technologies can learn effectively from high-quality, well-labeled data.
As a Data Labeling Analyst II, you will frequently work closely with data scientists and machine learning engineers to ensure datasets are accurately annotated for model training and validation. Collaboration often involves clarifying labeling guidelines, providing feedback on ambiguous cases, and adjusting annotation strategies based on project goals. Regular communication and review sessions help maintain consistency and high-quality data, which are vital for successful machine learning outcomes. This teamwork also offers opportunities to learn more about the end use of labeled data and to contribute ideas that improve overall data processes.
A Data Labeling Analyst II should possess strong attention to detail, familiarity with data annotation processes, and a background in computer science or a related field. Proficiency with data labeling tools such as Labelbox, Supervisely, or CVAT, as well as experience with database management systems, is typically required. Excellent communication, problem-solving abilities, and adaptability help analysts efficiently collaborate and manage evolving project requirements. These skills ensure the creation of high-quality labeled datasets, which are essential for training accurate machine learning models.
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