below marketmost similar roles pay hereabove market
This role pays more than
72%
of similar roles. Most pay
$126,800–$184,925
— the blue band above.
At the midpoint, this role pays about
$177k
versus about
$156k
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
711 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 668 roles with salary data.
JOB TITLE: Senior Data Scientist
The Senior Data Scientist joins the Sales Incentive Design team to design, scale, and improve analytical models powering sales quota setting and incentive programs. You will own quota methodologies, validate data assumptions, and partner with Finance and Analytics teams to translate financial objectives into scalable, explainable models. Day-to-day responsibilities include developing statistical, forecasting, and optimization models, identifying data-quality issues, and building AI-enabled applications. You will utilize Python, R, SQL/T-SQL, KQL, Spark, Azure Machine Learning, Azure Databricks, MLflow, LangChain, and GitHub Copilot. The role requires applying machine learning, RAG workflows, and prompt engineering to solve complex sales compensation problems. You must bridge the gap between technical model integrity and business impact, ensuring quota methodologies are accurate, transparent, and aligned with organizational sales strategy.
What does a Data Scientist earn in Remote?
Median $216300 from 39 postings across 16 companies.
Own and continuously improve sales quota models to ensure they are accurate, scalable, and aligned with business objectives.
Translate sales strategies, financial targets, and historical performance data into executable quota methodologies.
Validate quota-setting inputs, model logic, and outputs while identifying data-quality issues and model risks.
Develop statistical, forecasting, and machine-learning approaches to solve complex sales and business problems.
Prototype and operationalize AI-enabled applications and LLM workflows to improve sales incentive accuracy and scalability.
Build reproducible analytical workflows using Python, R, SQL, and Azure Machine Learning platforms.
Present complex quantitative findings and model recommendations to business stakeholders using data visualization and storytelling.
Maintain comprehensive documentation for methodology validation, model assumptions, and operational readiness.
What we're looking for
Must have a Doctorate with 1+ years of data science experience, a Master's with 3+ years, a Bachelor's with 5+ years, or equivalent experience.
Must have a Doctorate with 3+ years of data science experience, a Master's with 6+ years, a Bachelor's with 8+ years, or equivalent experience (preferred).
Must have 8+ years of experience in Data Science, Applied Statistics, Quantitative Analytics, Machine Learning, Operations Research, Sales Analytics, or a related quantitative field (preferred).
Must be proficient in programming and analytical skills using Python, R, SQL, KQL, or comparable languages (preferred).
Must have experience developing complex statistical, forecasting, optimization, or machine-learning models (preferred).
Must have experience with sales compensation, incentive compensation, quota setting, sales planning, territory planning, sales performance management, or revenue operations (preferred).
Must have experience building or operationalizing machine-learning or AI solutions using Azure Machine Learning, Azure Databricks, Spark, MLflow, or similar technologies (preferred).
Must have hands-on experience with LLMs, prompt engineering, RAG, vector databases, context engineering, or agentic AI systems (preferred).
Media Mix Modeling
Multi Touch Attribution
SQL
Python
R
Databricks
BigQuery
Machine Learning
Multivariate Regression
Optimization Algorithms
Generative AI
RShiny
Ads Data Hub
Data Science
Machine Learning
Python
R
SQL
Statistical Analysis
Data Pipelines
Feature Engineering
Causal Inference
Experimentation Frameworks
Model Monitoring
Responsible AI
Data Quality
Data Science
Optimization