Join our team of innovators at Microsoft, where you'll be part of shaping the future of cloud services. As a Data Scientist / Applied Scientist, you will contribute to one of Microsoft's fastest-growing cloud services, Microsoft 365, enabling personalized experiences that empower millions of users across the globe. About the Role - You will develop and evaluate ML models using prepared datasets, customer feedback, and novel training/fine-tuning algorithms for language models. - Collaborate closely with software engineers, product managers, and researchers to design, implement, and evaluate models and algorithms that continuously adapt and improve based on user interactions and feedback. - Leverage advanced machine learning and information retrieval to predict and optimize memory content selection, representation, and lifecycle management. About the Team The Substrate Core Substrate team powers the infrastructure that underpins Microsoft 365's most critical services. We are a high-impact, forward-looking team focused on building intelligent, scalable, and cost-efficient platforms that enable Microsoft to deliver world-class productivity experiences to billions of users. As a Data Scientist / Applied Scientist, you will work closely with the team to deliver end-to-end experimentation, evaluation, and insights to Copilot engineers, PMs, and fellow scientists. You'll work on the data generation platform that creates algorithms and ML pipelines for simulating user actions and datasets that reflect real-world usage and user preferences. Our mission is to drive operational excellence and innovation across the M365 fleet by leveraging AI, automation, and deep platform integration. We're seeking candidates with research skills and the desire to pursue the cutting edge in model development that pushes technological boundaries. Your Responsibilities - Drive customer-centric solutions by aligning with business goals and managing stakeholder expectations. - Collaborate cross-functionally to define success metrics and improve AI quality at scale. - Analyze evaluation outputs to identify gaps in coverage, quality, and usability. - Design experiments, define metrics, and develop ML pipelines for encoder-decoder and cross-encoder models, semantic search, and user intent understanding. What You'll Need - Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical/Computer Engineering, or related field. - 4+ years of experience in predictive analytics, statistics, or research. - Experience with synthetic data generation and data management for evaluation/training. - At least one year of experience publishing patents or peer-reviewed papers.