Senior Data Scientist

Quick Facts

• Role: Senior Data Scientist
• Focus: Sales quota setting and incentive program analytics; statistical and AI-enabled quota models

Description

Own and continuously improve sales quota and incentive methodologies by translating sales strategy and financial objectives into scalable, explainable statistical and AI models. Validate data inputs and assumptions, identify data-quality issues and model risks, and collaborate across Finance, Sales Compensation/Plan Design, Analytics, Operations, and data-owner teams to operationalize quota analytics. Build forecasting, optimization, and machine-learning approaches and help modernize quota decisions using advanced analytics, automation, and AI/LLM workflows.

Responsibilities

• Own and evolve quota models/methodologies to ensure accuracy, scalability, explainability, and alignment with compensation and business objectives
• Translate strategy, targets, budget assumptions, historical performance, run-rate data, and compensable metrics into executable quota methodologies
• Validate quota-setting inputs and outputs (baselines, historical data, assumptions, model logic, and results)
• Proactively identify data-quality issues, unsupported assumptions, model risks, and upstream dependencies
• Partner cross-functionally to align financial objectives with quota methodology and execution
• Explain model drivers, assumptions, limitations, tradeoffs, and required actions to business and technical stakeholders
• Apply judgment in ambiguous situations balancing model integrity, financial alignment, operational feasibility, and field impact
• Maintain methodology documentation, validation evidence, and model assumptions for recurring quota-setting cycles
• Develop statistical, forecasting, optimization, and machine-learning approaches for sales and business problems
• Analyze large datasets to find trends, relationships, anomalies, risks, and opportunities
• Apply forecasting, hypothesis testing, experimentation, causal analysis, supervised/unsupervised learning, and optimization
• Assess model performance for statistical validity and business impact; address bias/variance and methodological limitations
• Create scalable analytical models and partner to operationalize solutions
• Build reproducible workflows using Python/R/SQL and scalable platforms
• Partner on AI/LLM metrics and evaluation frameworks; prototype, build, validate, and operationalize AI-enabled tools
• Evaluate and improve AI systems; design and optimize RAG and related LLM workflows
• Maintain data quality, dataset preparation, change detection, and operational readiness
• Build readable, maintainable, extensible analytical code and documentation; support responsible AI and governance

Benefits

Eligible for benefits and other compensation (details provided via the Microsoft corporate pay information link).