Senior Data Scientist

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. 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. 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. 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. Develop analytical roadmaps that connect technical investments to measurable business outcomes. Build efficient, readable, maintainable, and extensible analytical code. Develop clear technical documentation supporting model validation, knowledge transfer, operational deployment, and ongoing governance. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) 8+ years of experience in Data Science, Applied Statistics, Quantitative Analytics, Machine Learning, Operations Research, Sales Analytics, or a related quantitative field.

developing complex statistical, forecasting, optimization, or machine-learning models.

Proficient programming and analytical skills using Python, R, SQL, KQL, or comparable languages.

analyzing large datasets and translating quantitative findings into actionable business recommendations.

Demonstrated ability to validate model inputs, assumptions, methodologies, and outputs.

partnering with cross-functional stakeholders across technical and business organizations.

with sales compensation, incentive compensation, quota setting, sales planning, territory planning, sales performance management, or revenue operations.

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.

with financial modeling, forecasting, optimization, or business planning in a large-scale sales organization.

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.

developing analytical solutions that operate on an enterprise scale.

Ability to influence senior stakeholders through data-driven recommendations, visualization, and storytelling.