Senior Data Scientist

** Overview*
* Microsoft is a company where passionate innovators come to collaborate, envision what can be, and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit of thinking in a cloud-enabled world

We are seeking a  
** Senior Data Scientist
** to help design, scale, and continuously improve the analytical models that power sales quota setting and incentive programs. This role sits at the intersection of Sales Incentive Design, Data Science, Statistical Modeling, AI, and Business Strategy.

You will own and evolve quota methodologies, validate the data and assumptions behind quota-setting decisions, and partner across Finance, Plan Design, Implementation, Analytics, and business teams to translate complex financial and business objectives into scalable, explainable models.

The role combines strong quantitative and technical depth with an understanding of sales compensation, quota methodologies, forecasting, financial modeling, or sales performance management. You should be equally comfortable analyzing complex datasets, developing models and AI-enabled solutions, and explaining recommendations to business and technical stakeholders.

Microsoft's mission is to empower every person and every organization on the planet to achieve more, and we're dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their highly qualified contributions each day. Join us and help shape the future of the world.

** What Success Looks Like?*
* Success in this role means delivering quota methodologies that stakeholders can trust, understand, operationalize, and defend. You will connect financial objectives and sales strategy to rigorous quantitative models while continuously improving the underlying data, methodology, and technology.

You will also help modernize how sales incentive and quota decisions are made by applying advanced analytics, automation, machine learning, and AI to improve accuracy, scalability, transparency, and business impact.

** Responsibilities*
* The  
** Senior Data Scientist
** is responsible for the following:

+ Own and continuously improve sales quota models and methodologies, ensuring they are accurate, scalable, explainable, and aligned with compensation design and business objectives.

+ Translate sales strategy, financial targets, budget assumptions, historical performance, run-rate data, and compensable metrics into executable quota methodologies.

+ Validate quota-setting inputs and outputs, including baselines, historical data, assumptions, model logic, and results.

+ Proactively identify data-quality issues, unsupported assumptions, model risks, and upstream dependencies that could affect quota accuracy or field outcomes.

+ Partner across Finance, Sales Compensation/Plan Design, Implementation, Analytics, Operations, and data-owner teams to align financial objectives with quota methodology and execution.

+ Provide quota-model expertise and analytical support to business and field stakeholders, clearly explaining model drivers, assumptions, limitations, tradeoffs, and required actions.

+ Apply sound judgment in ambiguous situations by balancing model integrity, financial alignment, operational feasibility, and potential field impact.

+ Maintain comprehensive methodology documentation, validation evidence, model assumptions, and operational readiness for annual quota-setting cycles and evolving compensable metrics.

** Data Science, Modeling & Statistical Analysis:*
* + Develop statistical, forecasting, optimization, and machine-learning approaches to solve complex sales and business problems.

+ Analyze large and complex datasets to identify performance trends, relationships, anomalies, risks, and opportunities.

+ Apply techniques such as forecasting, hypothesis testing, experimentation, causal analysis, supervised and unsupervised learning, and optimization where appropriate.

+ Assess model performance for both statistical validity and business impact.

+ Identify risks involving data quality, bias, variance, methodological limitations, and model assumptions.

+ Develop scalable analytical models and partner with engineering and data teams to operationalize solutions.

+ Build reproducible analytical workflows using technologies such as Python, R, SQL/T-SQL, KQL, Spark, Azure Machine Learning, Azure Databricks, MLflow, or similar platforms.

** AI & LLM Solutions:*
* +…