Data Analyst I – AI Annotation for Identity Verification

About the role

We are looking for a detail‑oriented Data Analyst I to support the development of secure and fair identity verification systems. You will work on labeling and validating diverse datasets that power document fraud detection, deep‑fake detection, presentation attack detection, and face‑recognition models.

Key responsibilities

• Perform data annotation across documents, images, video, and audio following project guidelines.
• Identify and label document elements such as names, photos, signatures, and holograms, and flag potential tampering.
• Annotate manipulated regions in deep‑fake and image‑tamper datasets.
• Tag liveness indicators and spoof artifacts in PAD datasets.
• Draw bounding boxes, facial landmarks, and expression labels for face‑recognition tasks.
• Apply time‑based labels in video for frame‑level PAD/deep‑fake detection.
• Document edge cases, suggest improvements to annotation guidelines, and review peer annotations for consistency.
• Collaborate with ML engineers and data scientists to improve dataset quality and tooling.

Required profile

• Strong attention to detail and observational skills.
• Ability to maintain accuracy while performing repetitive tasks.
• Basic understanding of computer‑vision and machine‑learning concepts (training provided).
• Comfortable using web‑based annotation tools (training provided).
• Clear written and verbal communication to describe edge cases.

Required skills

• Experience with annotation tools.

What we offer

• Opportunities to work on cutting‑edge AI projects in risk management.
• Collaborative engineering environment with continuous learning.