Senior Applied Scientist, AI Data Platform (CoreAI)

Join Microsoft's CoreAI team to build the AI Data Platform, the foundation for secure, scalable, reusable datasets that power model development. The AI Data Platform teams mission is to build a central AI data platform that breaks down Microsoft's data silos and manages the full lifecycle of first-party, third-party, synthetic, and human-labeled data, accelerating AI model development with secure, reusable, and compliant datasets. The AI Data Platform team is responsible for large-scale data infrastructure, automation tools, and intelligence services to transform how Microsoft collects, generates, manages, and shares AI training data. We are seeking Applied Scientists to drive scientific innovation in data generation, validation, evaluation, and automation. You will set the vision for intelligent, ML-driven services that manage the end-to-end data lifecycle, and partner with leaders across Microsoft to ensure Microsoft's data investments deliver maximum AI impact. Qualifications Required Qualifications Bachelors Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Masters Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 2+ years of experience applying machine learning or data science in practical settings. Programming skills in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Experience with data analysis, dataset design, or evaluation methodologies. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years Preferred Qualifications Master's degree or PhD in Computer Science, Machine Learning, Statistics, or related field, or equivalent experience. 4+ years of experience applying machine learning or data science in practical settings. Experience with LLM training pipelines, synthetic data generation, or data-centric AI approaches. Knowledge of PII detection, data privacy, fairness, or compliance in AI systems. Familiarity with distributed data systems (e.g., Spark, Databricks, Azure Data Lake). Strong collaboration skills with engineers, TPMs, and product partners across multiple orgs. Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Microsoft posts positions for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Responsibilities Advancing machine learning and data science  to improve data quality, automate dataset generation, and design intelligent agent-driven services that manage the end-to-end data lifecycle. Develop ML-based pipelines "¯for data generation, validation, augmentation, and discovery (e.g., synthetic data, human-in-the-loop workflows). Design and train intelligent agents "¯to automate key parts of the dataset lifecycle, including ingestion, validation, PII detection and handling, governance, discovery, and feedback loops. Build evaluation methods "¯to measure dataset quality, coverage, and usefulness for large-scale model training. Leverage AI/ML techniques "¯(e.g., classification, clustering, anomaly detection, embeddings, LLM-based evaluation) to improve data discovery, curation, and governance. Collaborate with engineers "¯to integrate scientific methods and models into scalable pipelines and platform services. Partner with AI product and research teams "¯(CoreAI, MAI, M365, GitHub, MSR, and more) to align datasets with model training needs and identify new opportunities. Contribute thought leadership "¯by publishing or sharing insights internally and externally to shape Microsoft's data-centric AI practices.

Created: 2025-09-26