Key Responsibilities
1. Project Management
Lead manage multiple AI data annotation projects (Audio/Video/LLM), ensuring on-time delivery, quality standards, project objectives are met.
Track progress milestones, identify mitigate risks, proactively drive solutions to ensure smooth project execution.
Build maintain strong collaboration with cross-functional teams, including Product, Research, Data Operations, Engineering, to align timelines, resolve blockers, drive project success.
2. Workflow Design & Optimization
Design, manage, continuously refine project workflows covering training setup, QA processes, performance tracking.
Partner with product managers project leads to ensure all workflow standards align with the team’s technical goals quality benchmarks.
3. Operational Excellence
Drive continuous improvement initiatives in data labeling, training efficiency, quality assurance.
Lead cross-domain optimization projects, establish best practices, maintain process documentation, technical guides, case manuals to ensure consistent delivery quality.
4. Data Monitoring & Analysis
Design implement data monitoring strategies to assess maintain the quality of training validation datasets.
Utilize statistical modeling, visualization, programming tools (Python: Pandas, NumPy, Matplotlib; SQL) to analyze annotation accuracy, dataset coverage, model performance.
Conduct shard-level evaluation, prompt sensitivity testing, clustered erranalysis to uncover data gaps, edge cases, failure patterns.
Collaborate with data model teams to turn analytical insights actionable strategies ftraining improvements iterative optimization.
Qualifications
1. Bachelor’s degree above, with 3+ years of experience in internet product operations AI data operations.
2. Strong communication problem-solving abilities.
3. Demonstrated expertise in managing complex projects designing scalable operational workflows.
4. Fluent in English, with proven ability to collaborate effectively with international teams.
5. Highly adaptable to dynamic, fast-paced, project-based environments.
6. Genuine passion fAI computational thinking.
7. Familiarity with full-stack concepts, including front-end, back-end, database integration.
8. Tech-savvy, data-driven, experienced with tools that improve project efficiency collaboration.
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