Whenever a customer visits Amazon and types in a query or browses through product categories, Amazon Search services go to work. The Search Ops team helps Search services in providing a better customer search experience by delivering quality data annotation to help improve AI/ML models driving these services.
Our vision is to create business value by delivering high quality data at scale. We look to provide easy and scalable labeling solution to support search that are high quality, cost efficient, and secure. Our vision is to enable improvement in the search experience for our customers, by accurately determining labels for products targeted by the search queries received. We collaborate closely with several machine learning (ML) applied science teams that develop and test ML models to improve the quality of semantic matching, ranking, computer vision, image processing, and augmented reality.
To support our vision, we need exceptionally talented, bright, and driven people. Duties will include ensuring that standards for productivity and quality assurance are met by your team, taking part in planning, organizing and directing the work of subordinates or others, and outlining procedures and instructions on work received, make time estimates on new jobs received, ensuring utilization of team is high, mentor and train new/existing team members. If you have what it takes then this is your chance to work hard, have fun, and make history.
Key job responsibilities
As a Lead Data Specialist, ML Data Ops, you will be responsible for meeting operational and business goals overlooking about 30-40 associates, having expertise in one or more processes/functions. You will also be a driving initiative across sites for process improvements, SoP and guidelines formulation, diving deep to provide data insights as and when required. Your key responsibilities will include (but not limited to) the below:
- Data Analysis: Conduct in-depth analysis of data to identify patterns, problems, root causes, and potential solutions, leveraging analytical tools and techniques.
- Stakeholder Collaboration: Collaborate effectively with relevant stakeholders to align data analysis efforts with business goals, ensuring insights drive decision-making and strategic initiatives.
- Escalation Management: Manage escalations by analyzing data, identifying trends and gaps, and reporting key metrics to facilitate informed decision-making and resolution.
- Process Improvement: Review standard operating procedures (SOPs), processes, and tools to proactively identify areas for improvement, striving to enhance quality metrics and operational efficiency.
- Continuous Improvement: Drive continuous improvement initiatives, actively contributing to the Correction of Error (COE) process by documenting data curation and annotation issues and suggesting improvements.
- Leadership Support: Participate in business reviews with mid-level and senior leadership, providing valuable insights and support to drive strategic objectives.
- Process On-boarding: Participate in the on-boarding of new processes or experiments, ensuring comprehensive documentation and smooth integration into existing workflows.
- Launch Plan Development: Develop robust launch plans for new team members and oversee progress tracking through the administration of launch plans, ensuring seamless integration and productivity.
- Training and Coaching: Coach new hires on process tasks and provide feedback to the training team for the customization of training modules, facilitating skill development and performance improvement.
- Backup Support: Serve as a backup for the identified manager and provide support to the respective team in various aspects, ensuring continuity of operations and effective team functioning.
- Quality Assurance: Perform quality checks on annotated data with a high level of precision, adhering to annotation guidelines and maintaining data integrity and accuracy.
- Sensitive Data Handling: Demonstrate willingness to work with sensitive data, including adult content, religious, and other sensitive issues, adhering to privacy and confidentiality protocols.
Our vision is to create business value by delivering high quality data at scale. We look to provide easy and scalable labeling solution to support search that are high quality, cost efficient, and secure. Our vision is to enable improvement in the search experience for our customers, by accurately determining labels for products targeted by the search queries received. We collaborate closely with several machine learning (ML) applied science teams that develop and test ML models to improve the quality of semantic matching, ranking, computer vision, image processing, and augmented reality.
To support our vision, we need exceptionally talented, bright, and driven people. Duties will include ensuring that standards for productivity and quality assurance are met by your team, taking part in planning, organizing and directing the work of subordinates or others, and outlining procedures and instructions on work received, make time estimates on new jobs received, ensuring utilization of team is high, mentor and train new/existing team members. If you have what it takes then this is your chance to work hard, have fun, and make history.
Key job responsibilities
As a Lead Data Specialist, ML Data Ops, you will be responsible for meeting operational and business goals overlooking about 30-40 associates, having expertise in one or more processes/functions. You will also be a driving initiative across sites for process improvements, SoP and guidelines formulation, diving deep to provide data insights as and when required. Your key responsibilities will include (but not limited to) the below:
- Data Analysis: Conduct in-depth analysis of data to identify patterns, problems, root causes, and potential solutions, leveraging analytical tools and techniques.
- Stakeholder Collaboration: Collaborate effectively with relevant stakeholders to align data analysis efforts with business goals, ensuring insights drive decision-making and strategic initiatives.
- Escalation Management: Manage escalations by analyzing data, identifying trends and gaps, and reporting key metrics to facilitate informed decision-making and resolution.
- Process Improvement: Review standard operating procedures (SOPs), processes, and tools to proactively identify areas for improvement, striving to enhance quality metrics and operational efficiency.
- Continuous Improvement: Drive continuous improvement initiatives, actively contributing to the Correction of Error (COE) process by documenting data curation and annotation issues and suggesting improvements.
- Leadership Support: Participate in business reviews with mid-level and senior leadership, providing valuable insights and support to drive strategic objectives.
- Process On-boarding: Participate in the on-boarding of new processes or experiments, ensuring comprehensive documentation and smooth integration into existing workflows.
- Launch Plan Development: Develop robust launch plans for new team members and oversee progress tracking through the administration of launch plans, ensuring seamless integration and productivity.
- Training and Coaching: Coach new hires on process tasks and provide feedback to the training team for the customization of training modules, facilitating skill development and performance improvement.
- Backup Support: Serve as a backup for the identified manager and provide support to the respective team in various aspects, ensuring continuity of operations and effective team functioning.
- Quality Assurance: Perform quality checks on annotated data with a high level of precision, adhering to annotation guidelines and maintaining data integrity and accuracy.
- Sensitive Data Handling: Demonstrate willingness to work with sensitive data, including adult content, religious, and other sensitive issues, adhering to privacy and confidentiality protocols.