This role pays less than
51%
of similar roles. Most pay
$161,250–$231,812
— the shaded band above.
At the midpoint, this role pays about
$177k
versus about
$197k
for comparable roles.
Based on 240 similar postings.
Employer
About Microsoft
Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing
Microsoft currently has
598 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 586 roles with salary data.
As a Senior Applied Scientist on the team responsible for online advertising auctions, you will advance auction mechanism design by extending proven approaches to new markets and developing novel solutions. You will evaluate existing algorithms, identify opportunities for improvement, and validate your findings through large-scale experimentation. Your daily work involves building data pipelines and software to support rapid implementation of solutions, dashboards, and diagnostics while incorporating research to meet business goals. To succeed in this role, you must apply expertise in mathematical and statistical modeling, machine learning, reinforcement learning, optimization methods, and Azure/Microsoft Data Science tools. You will solve complex problems related to the ranking and pricing of ads within the Microsoft Advertising ecosystem, utilizing your skills in mechanism design, A/B experimentation, and data pipeline creation to drive measurable impact for the business.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Machine Learning
Deep Learning
Large Language Models
Python
C++
C#
Java
PyTorch
TensorFlow
NLP
Distributed Training
Search
Statistics
Predictive Analytics
C
Large Language Models
Information Retrieval
Ranking Systems
Agentic AI
Retrieval-Augmented Generation
Supervised Fine-Tuning
Preference Optimization
Model Distillation
Machine Learning
A/B Testing
Distributed Training
Search Engines
Machine Learning
Anomaly Detection
Large Language Models
Deep Learning
Causal Inference
Statistical Machine Learning
Data Mining
System Design
Low-latency Pipelines
Experimentation Frameworks
Model Operationalization