As an ML Data Ops II, you will play a crucial role in managing and improving machine learning data annotation processes. Your responsibilities span a wide range of tasks, from executing and overseeing annotation work to ensuring quality, productivity, and compliance across ML data process areas. You'll serve as a point of contact for simple annotation tasks, modify SOPs, test new tools, and provide valuable feedback for improvements. Your analytical skills will be put to use as you track metrics, report progress, and identify process issues. Quality assurance is a key aspect of your role, involving auditing work, documenting errors, and performing root cause analysis. You'll also contribute to continuous process improvement by analyzing ML datasets and implementing small-scale simplifications. Additionally, you'll take on a leadership role by providing guidance and training team members, participating in knowledge sharing sessions.

Key job responsibilities
As an ML Data Ops II, your key responsibilities will encompass a wide range of tasks critical to the success of machine learning data annotation processes. You'll execute and oversee annotation tasks across multiple ML data process areas, serving as a point of contact for simple annotation tasks while achieving targeted KPIs. Your role involves modifying and documenting SOPs, testing new tools, and providing valuable feedback for improvements. You'll analyze data, track metrics, and report progress to stakeholders, while also reviewing process issues and ensuring compliance with guidelines. Quality assurance is a crucial aspect of your position. You'll contribute to continuous process improvement by analyzing ML datasets, suggesting and implementing small-scale process simplifications, and identifying operational issues. Ensuring team adherence to confidentiality and compliance requirements is paramount, as is conducting periodic data audits. Your analytical skills will be utilized to provide insights for ongoing process enhancements, and you'll collaborate with cross-functional teams to improve operational metrics and processes. This role offers opportunities for growth and development within the ML data operations field, with a focus on process improvement and quality assurance.