Remote Data Annotation Analyst Jobs in Kalispell, MT

As of Aug 25, 2026, the average yearly pay for remote data annotation analyst in Kalispell, MT is $81,983.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $96,200.00 per year, depending on experience, location, and employer.

Remote Data Annotation Analysts are professionals who label, categorize, or tag data—such as images, text, audio, or video—from a remote location. Their work helps train machine learning algorithms by providing structured datasets that computers can learn from. These analysts use specialized tools to identify relevant features in raw data, ensuring accuracy and consistency. The role often requires attention to detail, basic technical skills, and the ability to follow specific guidelines or instructions. This position is commonly found in industries like artificial intelligence, autonomous vehicles, and natural language processing.

To thrive as a Remote Data Annotation Analyst, you need strong attention to detail, analytical thinking, and a high school diploma or equivalent, with many roles preferring experience in data-related tasks. Familiarity with data annotation platforms (like Labelbox or AWS SageMaker Ground Truth) and basic understanding of data management tools are typically required. Excellent time management, self-motivation, and clear communication help analysts manage remote workloads and collaborate effectively with distributed teams. These skills ensure accurate, high-quality annotated data essential for training and validating machine learning models.

As a Remote Data Annotation Analyst, you’ll frequently work within a distributed team, using collaboration tools such as Slack, project management platforms, and shared annotation guidelines. Regular virtual meetings and feedback sessions help ensure everyone applies labeling standards consistently and resolves ambiguities. It’s common to review peer annotations and participate in quality assurance checks, promoting a culture of accuracy and continuous improvement. Clear communication and attention to detail are essential for maintaining high-quality annotated datasets across the team.