Senior Applied Scientist at Microsoft

Department: Applied Sciences

Compensation

$119,800 – $234,700/yr

Setup: On-site
Location: Redmond, Washington · Sunnyvale, California
Type: Full-time
Level: senior
Posted: May 14, 2026

Description

Microsoft is hiring an Applied Scientist focused on ad interaction outcome prediction under partial and noisy feedback. The role develops estimated conversion models, data-driven attribution and weak-label generation pipelines, multi-task and proxy-signal models, calibration methods, and production systems that improve ad ranking, bidding, advertiser ROI, and user experience. Candidates need a relevant bachelor's, master's, or doctorate degree plus the specified years of related experience, along with strong machine learning, Python, and large-scale systems experience. The role offers a typical U.S. base pay range of USD $119,800-$234,700 per year, with higher ranges for the San Francisco Bay area and New York City metropolitan area.

Required Skills

Transformer models
Attribution modeling
Model calibration
Python
PyTorch
Feature engineering
A/B experimentation

Additional Information

Minimum Education: Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field
Required Previous Experiences: 4+ years of related experience in statistics, predictive analytics, or research
3+ years of related experience in statistics, predictive analytics, or research
1+ year of related experience in statistics, predictive analytics, or research
4+ years of industry experience building and shipping machine learning models in production
Preferred Qualifications: Master's degree in a relevant field with 6+ years of related experience
Doctorate in a relevant field with 3+ years of related experience
3+ years of publication experience
Invited conference or industry presentation experience
3+ years of research experience
1+ year of live production systems experience
1+ year of product development experience
Experience with noisy, weak, or proxy labels
Conversion, outcome, or funnel modeling experience
Model calibration and uncertainty estimation
Causal inference or counterfactual evaluation
Large-scale marketplace or recommendation systems
Multi-task learning systems
Technical leadership in cross-team or platform ML systems
Hands-on experience with modern machine learning models and feature engineering
Supervised and multi-task learning
Large-scale data and end-to-end modeling pipelines
Offline evaluation and online A/B experimentation
Python and major machine learning framework
Independent modeling project leadership

About Microsoft

Microsoft

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