Amazon
Language Data Scientist II, Amazon Q Business
Amazon Q Business is an AI assistant powered by generative technology. It provides capabilities such as answering queries, summarizing information, generating content, and executing tasks based on enterprise data. We are seeking a Language Data Scientist II to join our data team. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Q Business. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users. As part of our diverse team—including language engineers, linguists, data scientists, data engineers, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Key job responsibilities: Oversee end-to-end evaluation data pipeline and propose evaluation metrics and methods. Incorporate your knowledge of linguistic fundamentals, NLU, NLP to the data pipeline. Process and analyze diverse media formats including audio recordings, video, images and text. Perform statistical analysis of the data. Write intuitive data generation & annotation guidelines. Write advanced and nuanced prompts to optimize LLM outputs. Write python scripts for data wrangling. Automate repetitive workflows and improve existing processes. Perform background research and vet available public datasets on topics such as long text retrieval, text generation, summarization, question-answering, and reasoning. Leverage and integrate AWS services to optimize data collection workflows. Collaborate with scientists, engineers, and product managers in defining data quality metrics and guidelines. Lead dive deep sessions with data annotators. About the team Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Minimum Requirements: 2+ years of data scientist experience. 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. Excellent knowledge of semantics, pragmatics, conversation analysis, and/or discourse analysis. Experience designing and executing data collection projects, including guidelines, label set and annotation workflow development. Experience developing and evaluating data annotation and data quality metrics. Experience designing and executing psychology/linguistic/cognitive science surveys or experiments with human participants. - Master’s Degree or higher in Linguistics, Computational Linguistics, Natural Language Processing (NLP), or other related field. - Knowledge of languages other than English. Hands-on experience with LLMs, prompt engineering, machine learning and deep learning techniques in the fields of NLP. Experience with AWS services (S3, Sagemaker, ML language services, etc.). Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.
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Amazon Q Business is an AI assistant powered by generative technology. It provides capabilities such as answering queries, summarizing information, generating content, and executing tasks based on enterprise data. We are seeking a Language Data Scientist II to join our data team. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Q Business. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users. As part of our diverse team—including language engineers, linguists, data scientists, data engineers, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Key job responsibilities: Oversee end-to-end evaluation data pipeline and propose evaluation metrics and methods. Incorporate your knowledge of linguistic fundamentals, NLU, NLP to the data pipeline. Process and analyze diverse media formats including audio recordings, video, images and text. Perform statistical analysis of the data. Write intuitive data generation & annotation guidelines. Write advanced and nuanced prompts to optimize LLM outputs. Write python scripts for data wrangling. Automate repetitive workflows and improve existing processes. Perform background research and vet available public datasets on topics such as long text retrieval, text generation, summarization, question-answering, and reasoning. Leverage and integrate AWS services to optimize data collection workflows. Collaborate with scientists, engineers, and product managers in defining data quality metrics and guidelines. Lead dive deep sessions with data annotators. About the team Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Minimum Requirements: 2+ years of data scientist experience. 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. Excellent knowledge of semantics, pragmatics, conversation analysis, and/or discourse analysis. Experience designing and executing data collection projects, including guidelines, label set and annotation workflow development. Experience developing and evaluating data annotation and data quality metrics. Experience designing and executing psychology/linguistic/cognitive science surveys or experiments with human participants. - Master’s Degree or higher in Linguistics, Computational Linguistics, Natural Language Processing (NLP), or other related field. - Knowledge of languages other than English. Hands-on experience with LLMs, prompt engineering, machine learning and deep learning techniques in the fields of NLP. Experience with AWS services (S3, Sagemaker, ML language services, etc.). Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.
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