Keystone is a premier strategy, technology, and economics firm that delivers science-led AI solutions for Fortune 500 companies. We design, deploy, and operate machine learning software that automates and optimizes complex operational and commercial decisions. Our CoreAI Solutions Group includes world-class AI and ML practitioners with unmatched experience implementing large-scale, high-impact models that help enterprises make smarter decisions across manufacturing, supply chain, sales, and marketing. We bring transformative ideas to life—and ensure they scale, endure, and deliver measurable value. This position is based in our Bellevue, WA office. Position SummaryWe are seeking a motivated and detail-oriented Data Analyst Intern to join our analytics team. The intern will assist in data preparation, analysis, and labeling efforts to support model training, evaluation, and decision-making. This is an exciting opportunity to work at the intersection of data science and artificial intelligence.Key Responsibilities Collect, clean, and validate datasets from internal and external sources Conduct data labeling and annotation tasks, ensuring consistent and accurate categorization of structured and unstructured data (e.g., images, text, audio, or tabular inputs) Collaborate with data scientists to understand labeling requirements and define annotation guidelines Perform exploratory data analysis (EDA) to uncover trends, correlations, and patterns Assist in building dashboards, reports, and visualizations using tools like Excel, SQL, and Tableau or Power BI Document data labeling procedures, labeling schemas, and quality assurance protocols Support the development and automation of reporting processes Collaborate with cross-functional teams to support a wide range of business and AI-driven projects Required Qualifications Currently pursuing a Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Economics, or a related field Strong attention to detail and commitment to data accuracy and consistency Proficiency in Excel and at least one programming/query language (e.g., SQL, Python, R) Familiarity with data annotation tools or platforms (e.g., Labelbox, Prodigy, Snorkel, Amazon SageMaker Ground Truth) is a plus Excellent communication and documentation skills Ability to work independently on repetitive tasks while maintaining high-quality output Preferred Qualifications Understanding of supervised learning and its dependence on high-quality labeled datasets Experience labeling or curating datasets for ML/AI models Familiarity with unstructured data formats (e.g., text, images, video, audio) ·Previous internship or coursework involving data processing or human-in-the-loop systems
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