Data Operations Analyst (AI Data & Safety)

Responsibilities

• In close partnership with internal researchers, industry experts, and leading data vendors, we tackle challenging data problems at the frontier of AI development, helping improve both model performance and user experience.
• As more large AI models are being developed, high-quality data has become the core fuel driving the leap in model capabilities. Our team - AI Data & Safety - Data Annotation and Evaluation Operations - is the builder and operator of this critical link.
• Our specific responsibilities include providing data training, model evaluation, model operation, and user growth for ByteDance's large model business, driving continuous improvement and application of model capabilities.
• Applications will be reviewed on a rolling basis - we encourage you to apply early.
• Your Role Will Involve:
• Project Management: Lead and manage data annotation, evaluation, and/or user growth projects for various AI product modalities (LLM, VLM, Speech or Video) across multiple General, STEM, or non-STEM academic topics. Ensure that timelines, quality standards, and objectives are met appropriately with meticulous planning. Track project progress, identify risks, and implement corrective actions as necessary to keep projects on course. Build and maintain strong relationships with product managers, researchers, data annotators, and other cross-functional team members. Communicate project updates, address concerns, and align expectations to ensure successful project outcomes. Engage with external vendors and experts per project demands and scale project productivity.
• Workflow Design and Management: Design, manage, and optimize workflows for each project you own, including training design, data annotation or QA processes, and performance tracking to meet project needs. Proactively plan and perform quality and productivity improvements to enhance operational processes. Develop and maintain technical guidelines and casebooks to support consistent, high-quality data production. Collaborate closely with product managers, project leaders, cross-functional teams and external collaborators to ensure alignment on quality metrics and project expectations.
• Data Checking and Analysis: Design and implement robust data analysis strategies to evaluate training and evaluation datasets. Ensure the mathematical accuracy and statistical validity of all project data. This includes designing and implementing robust data checking protocols, performing deep-dive analysis to identify trends and anomalies, and translating quantitative findings into actionable insights for model improvement in reports. You will collaborate with data annotators, researchers, and product managers to define quality benchmarks and ensure data-driven decision-making throughout the project lifecycle.
• Continuous Learning: Regularly follow the progress of competitor large models and related cutting-edge technologies, continuously explore efficient data production methods such as automated data scraping, model evaluation, and Agentic/Code-based data synthesis, and become an expert in one or more content verticals (such as Pre-training, Math or MultiModal Machine Learning). Foster a collaborative environment, sharing new learnings and best practices for knowledge transfer within the team.

Requirements

• Minimum Qua

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