This role pays more than
73%
of similar roles. Most pay
$126,800–$184,125
— the shaded band above.
At the midpoint, this role pays about
$177k
versus about
$155k
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
686 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 666 roles with salary data.
As a Senior Data Scientist within the Cloud and AI Platforms Monetization team, you will lead end-to-end yield optimization initiatives for Azure infrastructure, including virtual machines and storage. You will translate complex revenue, hardware, and capacity data into actionable insights to balance resource utilization, cost efficiency, and customer experience. Your daily work involves writing code in Python, SQL, and KQL to analyze large-scale datasets while developing machine learning models and causal inference techniques like difference-in-differences or synthetic control. You will design experiments, estimate demand elasticity, and create dashboards to influence cross-functional decisions regarding pricing and promotions. The role requires expertise in cloud economics and optimization techniques to solve problems related to resource allocation and yield management. You will also provide mentorship through code reviews and share best practices with technical and executive audiences.
What does a Data Scientist earn?
Median $162000 from 284 postings across 56 companies.
Analyze large-scale revenue, hardware, and capacity datasets using Python, SQL, or KQL to identify pricing and yield opportunities.
Develop and validate machine learning and statistical models to optimize resource allocation and pricing strategies.
Design experiments and use causal inference models to measure the impact of optimization hypotheses.
Estimate demand elasticity and customer substitution effects to inform product pricing and promotions.
Create decision-relevant metrics, dashboards, and narratives to translate complex data into actionable business recommendations.
Present findings to technical and executive audiences to influence cross-functional decisions on yield initiatives.
Apply knowledge of cloud economics and infrastructure to balance resource utilization with cost efficiency and customer experience.
What we're looking for
A Doctorate in a relevant field and 1+ years of data science experience, or a Master's degree and 3+ years of experience, or a Bachelor's degree and 5+ years.
Advanced proficiency in Python, SQL, KQL, or similar languages to analyze large-scale datasets.
Experience applying statistical techniques, machine learning, and causal inference models like difference-in-differences or synthetic control.
Ability to design experiments, measure impact, and estimate demand elasticity and customer substitution effects.
Experience with yield or revenue management, pricing optimization, or cloud resource allocation (preferred).
Advanced degree with 3+ years experience for a Doctorate, 6+ for a Master's, or 8+ for a Bachelor's (preferred).
Ability to translate complex data into actionable insights and compelling narratives for technical and executive audiences.
Knowledge of Azure pricing, cloud economics, and customer workflows to inform business trade-offs.
Python
SQL
Java
Machine Learning
Statistics
Spark
Scala
R
scikit learn
TensorFlow
Data Visualization
Natural Language Processing
Recommendation Systems
Optimization Models
WCAG) 2.2 AA