This role pays more than
68%
of similar roles. Most pay
$180,500–$246,487
— the shaded band above.
At the midpoint, this role pays about
$231k
versus about
$213k
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
599 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 564 roles with salary data.
The Principal Applied Science Manager - Excel Team leads a multidisciplinary team of researchers and applied scientists to advance agentic AI experiences within Microsoft Excel. This leader defines the scientific direction for agent architecture, model selection, prompting, and human-agent collaboration while establishing release-readiness criteria for new capabilities. The role involves translating frontier research into production-ready features by managing evaluation systems, performing trajectory analysis, and balancing quality, latency, reliability, safety, and cost across various models. Key responsibilities include building robust operating mechanisms for model comparisons, conducting technical decision-making, and fostering partnerships with internal teams and external AI laboratories. The position requires expertise in LLMs, agent architectures, benchmarking, and experimentation to solve complex spreadsheet tasks ranging from data analysis to model building through the integration of frontier foundation models into high-quality, scalable product capabilities.
Large Language Models
Deep Learning
Information Retrieval
Agent Orchestration
Machine Learning
Retrieval-Augmented Generation
Prompt Optimization
Fine-tuning
Domain Adaptation
Data Mining
Program Synthesis
AI Evaluation
Telemetry
Production Deployment
Statistics
Predictive Analytics
Python
Machine Learning
Retrieval-Augmented Generation
Large Language Models
Data Engineering
OCI
knowledge-graph s
Data Pipelines
Statistical Analysis
Model Evaluation
Experimental Design
Bioinformatics
Genomics