Senior Applied Scientist · Microsoft

Tue, 28 Apr 2026 13:07:17 GMT
Engineering

Microsoft · Sunnyvale · hybrid

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 transformer-based models with billions of parameters that power ad ranking, pricing, and optimization across large-scale consumer surfaces. These models go well beyond simple click prediction: they reason over long user histories, rich ad and content representations, and heterogeneous event streams to infer user intent and advertiser value, even when ground truth signals are sparse or partially unobservable.

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, and deploy models that remain robust under delayed conversions and shifting marketplace dynamics.

This is a hands-on role with real ownership: you’ll help shape next-generation transformer architectures, push the limits of scalable training and serving, and see your models make measurable impact in one of the world’s largest ads ecosystems.

Responsibilities:

Drive modeling and data innovations for ad interaction outcome prediction under partial and noisy feedback.

Focus on building estimated conversion models, designing data-driven attribution and weak-label generation pipelines, and developing robust learning and calibration methods for scenarios where true user outcomes are sparse, delayed, or unobservable.

Design and evaluate multi-task and proxy-signal models, improve offline and online measurement frameworks, and translate modeling advances into production-ready systems that directly impact ad ranking, bidding, advertiser ROI, and user experience at web scale.

Qualifications:

Bachelor’s 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 Master’s 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.

Preferred Qualifications:

Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.

3+ years experience conducting research as part of a research program (in academic or industry settings).

1+ year(s) experience developing and deploying live production systems, as part of a product team.

1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Experience working with noisy, weak, or proxy labels, including building training signals from indirect user behavior.

Experience with conversion, outcome, or funnel modeling (e.g., post-click modeling, engagement modeling, attribution, or similar problems).

Familiarity with model calibration, reliability analysis, or uncertainty estimation in production systems.

Background in causal inference, attribution, or counterfactual evaluation.

Experience with large-scale online marketplaces or ads/recommendation systems.

Experience designing or operating multi-task / auxiliary-task learning systems.

Proven technical leadership in cross-team modeling efforts or platform-level ML systems.

4+ years of industry experience building and shipping machine learning models in production.

Solid hands-on experience with modern ML models (e.g., deep learning, tree-based models, or linear models) and feature engineering.

Solid 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.

Solid 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.