Applied Scientist II

Our team (Signals Modeling) builds the core intelligence that understands and predicts how users interact with ads - from the first impression through clicks, post-click engagement, and downstream business outcomes.  We design and train text and numerical models with billions of parameters that power ad ranking across large-scale consumer surfaces. The team owns end-to-end ML systems, including large-scale data and label construction, representation learning, multi-task and proxy objectives, calibration, and rigorous offline and online evaluation. We build sophisticated training pipelines that transform weak signals (e.g., page visits, dwell time, or engagement events) into high-quality learning targets. Engineers and scientists on the team work at the intersection of deep learning, large-scale experimentation, and marketplace economics, shipping production-grade models and data pipelines that directly drive revenue and advertiser ROI. This is a hands-on builder role where you will see your models make measurable impact in one of the world’s largest ads ecosystems. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.   Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Drive modeling and data innovations for user response modeling for advertising. Develop rigorous evaluation and experimentation frameworks to assess impact of model improvements on the marketplace. Own model training and data pipelines end-to-end, ensuring the reliability and overall health of the modeling stack.

Required Qualifications: Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree 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 Doctorate in Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) Experience with large-scale online marketplaces or ads/recommendation systems. Proven technical ability in cross-team modeling efforts or platform-level ML systems. 2+ years of industry experience building and shipping machine learning models in production. Proven experience with modern ML models (e.g., deep learning, tree-based models, or linear models) and feature engineering. Understanding of supervised learning and multi-task learning. Practical experience working with large-scale, real-world data and building end-to-end modeling pipelines (data preparation, training, validation, deployment). Experience with offline evaluation and online A/B experimentation for ML systems. Proven programming skills in Python and at least one major ML framework (e.g., PyTorch or TensorFlow). Ability to independently drive modeling projects from problem definition through production and iteration. #MicrosoftAI  Applied Sciences IC3 - The typical