Senior Data Scientist

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:

• Partner with stakeholders to define metrics and evaluation frameworks for AI, large language model (LLM), and agentic solutions. 

• 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. 

• Leverage tools and platforms such as Azure AI Foundry, Azure Machine Learning, LangChain, GitHub Copilot, or comparable technologies. 

Business Partnership & Impact :

• Partner with business and technical stakeholders to understand complex business problems and translate them into analytical solutions. 

• Develop analytical roadmaps that connect technical investments to measurable business outcomes. 

• Present complex quantitative concepts, model assumptions, findings, and recommendations in clear business terms. 

• Use storytelling, data visualization, and analytical evidence to influence decisions and drive stakeholder alignment. 

• Help stakeholders understand both the capabilities and limitations of models and underlying data. 

• Identify opportunities to improve business processes through data, automation, analytics, machine learning, and AI. 

Data Quality & Operational Excellence: 

• 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. 

Other 

• Embody our culture and values.  

Preferred Qualifications:  

• 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)  

• • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ 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 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)  

• • 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.