Senior Data Scientist

As a Senior Data Scientist at Microsoft in the United States (onsite), you will design, scale, and continuously improve analytical models that support sales quota setting and incentive programs. The work focuses on owning quota methodologies end-to-end, validating the data and assumptions behind them, and partnering across organizations to translate financial and business objectives into models that are accurate, explainable, and operationally ready.

What you will do

• Own and continuously improve sales quota models and methodologies to ensure they are accurate, scalable, and explainable while staying aligned to 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 impact 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, explaining model drivers, assumptions, limitations, tradeoffs, and required actions.
• Use 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.
• Develop statistical, forecasting, optimization, and machine-learning approaches for complex sales and business problems.
• Analyze large and complex datasets to uncover performance trends, relationships, anomalies, risks, and opportunities.
• Apply forecasting, hypothesis testing, experimentation, causal analysis, supervised and unsupervised learning, and optimization where appropriate.
• Assess model performance across both statistical validity and business impact.
• Identify risks related to 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 Python, R, SQL/T-SQL, KQL, Spark, Azure Machine Learning, Azure Databricks, MLflow, or similar platforms.
• Define metrics and evaluation frameworks for AI, large language model (LLM), and agentic solutions in partnership with stakeholders.
• Prototype, build, validate, and operationalize AI-enabled applications and analytical tools for internal customers.
• Evaluate and select appropriate models, prompting strategies, data sources, retrieval approaches, and context-engineering techniques for specific business problems.
• Design and optimize retrieval-augmented generation (RAG) and other LLM workflows to improve the quality and reliability of AI-generated outputs.
• Continually evaluate model performance and identify opportunities to refine AI systems.
• Use tools and platforms such as Azure AI Foundry, Azure Machine Learning, LangChain, GitHub Copilot, or comparable technologies.
• Translate complex business problems into analytical solutions with both technical and business stakeholders.
• Develop analytical roadmaps connecting technical investments to measurable business outcomes.
• Present complex quantitative concepts, model assumptions, findings, and recommendations in clear business terms, using storytelling, data visualization, and analytical evidence to drive stakeholder alignment.
• Help stakeholders understand model capabilities and limitations, as well as underlying data considerations.
• Identify opportunities to improve business processes through data, automation, analytics, machine learning, and AI.
• Acquire, prepare, and validate datasets required for modeling and business analysis.
• Detect changes in source data, business assumptions, performance patterns, or expected outcomes that could materially affect model results.
• Partner with data owners to improve data integrity, completeness, and reliability.
• Build efficient, readable, maintainable, and extensible analytical code.
• Develop clear technical documentation supporting model validation, knowledge transfer, operational deployment, and ongoing governance.
• Promote appropriate standards for responsible AI, privacy, data quality, and analytical integrity.
• Embody Microsoft culture and values.

Required qualifications

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ years data-science experience (for example, managing structured and unstructured data, applying statistical techniques, and reporting results)
• OR Master’s degree in a related field AND 3+ years data-science experience
• OR Bachelor’s degree in a related field AND 5+ years data-science experience
• OR equivalent experience.
• 8+ years of experience in Data Science, Applied Statistics, Quantitative Analytics, Machine Learning, Operations Research, Sales Analytics, or a related quantitative field.
• Experience developing complex statistical, forecasting, optimization, or machine-learning models.
• Proficient programming and analytical skills using Python, R, SQL, KQL, or comparable languages.
• Experience analyzing large datasets and translating quantitative findings into actionable business recommendations.
• Demonstrated ability to validate model inputs, assumptions, methodologies, and outputs.
• Experience partnering with cross-functional stakeholders across technical and business organizations.
• Experience with sales compensation, incentive compensation, quota setting, sales planning, territory planning, sales performance management, or revenue operations.
• Experience designing or managing quota allocation, quota methodology, target setting, capacity planning, or sales forecasting models.
• Knowledge of compensation-plan mechanics and the relationship between financial targets, performance metrics, quotas, and incentive outcomes.
• Experience with financial modeling, forecasting, optimization, or business planning in a large-scale sales organization.
• Experience building or operationalizing machine-learning or AI solutions using Azure Machine Learning, Azure Databricks, Spark, MLflow, or similar technologies.
• Hands-on experience with LLMs, prompt engineering, RAG, vector databases, context engineering, or agentic AI systems.
• Experience developing analytical solutions that operate on an enterprise scale.
• Ability to influence senior stakeholders through data-driven recommendations, visualization, and storytelling.

Technologies

• Python, R, SQL, T-SQL, KQL
• Spark, Azure Machine Learning, Azure Databricks, MLflow
• Azure AI Foundry, LangChain, GitHub Copilot
• LLMs, RAG, vector databases

What success looks like

• Deliver quota methodologies that stakeholders can trust, understand, operationalize, and defend.
• Connect financial objectives and sales strategy to rigorous quantitative models while continuously improving the underlying data, methodology, and technology.
• Modernize sales incentive and quota decisioning by applying advanced analytics, automation, machine learning, and AI to improve accuracy, scalability, transparency, and business impact.

Preferred qualifications

• Doctorate in a related field AND 3+ years data-science experience
• OR Master’s degree in a related field AND 6+ years data-science experience
• OR Bachelor’s degree in a related field AND 8+ years data-science experience
• OR equivalent experience.

Compensation: USD 119,800 - 261,000 per year.

Location: United States (onsite).