Data Annotation Specialist - Arlington, VA

At Figure Eight Federal, a Data Annotation Specialist focuses on reviewing and labeling various forms of data, such as audio, video, imagery, and text. This role is critical in preparing training data for AI and Machine Learning, ensuring high levels of accuracy and detail.

To be eligible for the Data Annotation Specialist position at Figure Eight Federal, candidates should have at least a high school diploma or equivalent. Ideal candidates are organized, have strong verbal and written communication skills, and are able to work well both independently and in team settings.

While not strictly required, having prior experience in an office environment or familiarity with intelligence analysis, especially in imagery analysis, is highly desirable for the Data Annotation Specialist role at Figure Eight Federal.

The working environment for a Data Annotation Specialist at Figure Eight Federal is collaborative and team-oriented. You'll be working closely with a diverse group of individuals, making strong communication skills essential for completing daily tasks effectively.

Yes, Data Annotation Specialists at Figure Eight Federal are expected to be self-motivated and capable of working independently while still being cooperative and communicative with team members for optimal project outcomes.

Key skills that enhance performance as a Data Annotation Specialist at Figure Eight Federal include excellent analytical abilities, attention to detail, proficiency in Microsoft Office, and the ability to adapt to changing priorities and handle ambiguity.

At Figure Eight Federal, Data Annotation Specialists can grow by enhancing their analytical skills, gaining experience in AI-related projects, or pursuing further education, leading to higher positions in data analysis or project management within the company.

As a Data Annotation Specialist, your primary responsibility is to review and label datasets according to predefined guidelines. You must ensure that all data is accurately tagged to contribute effectively to AI training data development. Be prepared to provide specific examples of how you ensure precision in your work.

Maintaining accuracy involves a meticulous approach to labeling data. I recommend double-checking your work, utilizing any available reference materials, and asking for feedback when uncertain. Providing examples from past experiences can highlight your attention to detail.

Collaboration is crucial. Think of a scenario where you worked in a team to achieve a specific goal. Discuss your role, how you communicated with team members, and what the outcome was. Highlight your team spirit and how you navigated challenges together.

In fast-paced environments, being flexible is key. Discuss strategies you use to adapt, such as prioritizing tasks, maintaining clear communication with your team, and assessing the impact of changes on your workload. Providing a real-life example can be impactful.

Mention any tools or platforms you’ve worked with that are related to data annotation, including CVAT or similar software. Discuss your comfort level in using these tools and how you quickly adapt to new technologies.

Express enthusiasm for AI technologies and their applications in defense and intelligence. Articulate how Figure Eight Federal’s mission resonates with your career goals and passion for working with data in meaningful ways.

Consistency is critical for effective data annotation. You should describe your process for familiarizing yourself with annotation guidelines and regularly cross-referencing your work with standards or peers to ensure uniformity.

Articulate any relevant experience, whether it’s academic projects, internships, or professional roles. Highlight how your background will help you contribute effectively to developing data for AI and Machine Learning applications.

Discuss your openness to feedback and continuous improvement. Provide examples of how you have taken constructive criticism to enhance your work quality and overall effectiveness as a data annotator.

Communication and attention to detail stand out. Discuss how these qualities are essential for successful collaboration and precise work in developing high-quality data for AI systems. Share experiences that illustrate these qualities in action.