As an Annotation Specialist I am responsible for: Use in-house tools to transcribe and annotate data for various projects. A few examples may be providing transcription (text) for audio clips or annotating text or images to verify certain types of data. Go through content (online and offline), extract information, provide needed information through tools with high accuracy and consistency. Performed quality evaluation of transcription and annotation results produced by different sources. Collaborated with team members on day-to-day problem solving and projects assigned by managers. Reviewing and understanding the requirements of data annotation projects like SBS, Readability, Special Data, WD Data, Hindi Language Data. Working on product policy analysis and identifying policy risks by modeling, experimentation, and causal inference. Leverage the diverse perspectives and experiences of team members to improve the quality and innovation of our centralized operational processes and stakeholder outcomes. Created and maintained MIS reports to track the accuracy of data for teams and delivered reports on time to meet deadlines. As a Data Annotation Specialist, I am comfortable in understanding data related to: Sentiment: The emotional intonation and other subjective implications behind keywords and phrases. Intent: The human intent, or the users end goal, behind different statements. Semantic: Understanding exactly what the meaning is behind what users are seeking. Use deductive reasoning based on information provided in contextual turns to ensure data labelling is of the highest quality.

Handling English and Hindi language data, primarily in the areas of text annotation, workflow solution development and general data analysis deliverables. Spearheaded the development of Data Annotation tagging word to word for more accurate results and emotion tagging capabilities for Siri (AI) in Apple CarPlay, Apple HomePod devices enabling precise identification and quantification of user emotions on a 1 to 10 scale. This data-driven approach provides actionable insights to enhance overall customer success. Ensured data accuracy at a rate of 95% while meticulously annotating critical data, facilitating its smooth transfer to the Data Scientist for analysis. This contributed to data-driven insights, empowering various verticals within the Customer Success Group and accelerating project timelines. Collaborated with the Workflow Management team and Operations to optimize the shrinkage dashboard by utilizing data analysis and visualization tools, effectively streamlining processes. This reflects an ability to navigate seamlessly through different teams to achieve desired outcomes. Identified business process solutions that increase efficiencies by listening and transcribing audio files and evaluating Siris response and language usage. Converting unstructured/raw data to structured data in order to be utilized by the engineering analysts. Improving goal achievement rate for AI (Siri) by recognizing, advancing and actualizing creative plans to meet and surpass execution and quality objectives set by leadership. Handling unique data analysis requests from a range of different customers to improve and enhance user experience with Siri (AI). Leverage the diverse perspectives and experiences of team members to improve the quality and innovation of our centralized operational processes and stakeholder outcomes.

Provided data creation and analytics services to help develop, test, and train the Alexa AI. Worked closely with Amazon Machine Learning (ML) science and modelling teams providing critical data they need to improve Alexas speech recognition and language understanding capabilities. Managed a team of 5 new hires in English-UK team for a special project and trained them to get maximum accuracy and productivity by doing analysis to make the work easier for us. In this phase I have prepared reports on daily productivity status and error categories. Played an integral role in the Alexa Editorial project for Alexa Devices with a primary focus on enhancing the system's ability to generate contextually relevant, accurate and engaging answers to customer queries through an AI device. This involved coordinating cross-functional teams, including writers, editors, and AI specialists, to ensure the timely delivery of high-quality content. Trained and mentored a team of associates in mastering the Alexa Editorial project, resulting in consistent achievement of a 90% quality rate which in turn resulted in in a 20% improvement in overall team performance. This proactive approach aligns with the role's requirement for versatile problem-solving. Worked with text, speech and other types of data and attach tags to the contents at scale. Delivered high quality of labelled data leveraging guidelines to meet specific key performance indicators and using in-house tools and software. Also reported issues with tools and software to the development team when necessary.