Senior Data Scientist

• Defines quota-setting strategy aligned with business, customer, and solution objectives
• Partners cross-functionally to identify and pursue opportunities for applying machine learning and other data-science methods to quota and incentive design
• Bridges Finance, Sales, Business Sales Operations, and Product teams through deep technical expertise
• Drives cross-discipline collaboration and leads efforts to refine intellectual property definitions and methodology improvements
• Educates field managers and sales leaders on quota methodology, data inputs, and model mechanics through roadshows, workshops, and ongoing enablement — ensuring transparency and building trust in the quota-setting process
• Demonstrate effective business acumen by aligning data science initiatives with strategic goals and proactively identifying opportunities for innovation and optimization
• Leverage an understanding of incentive compensation design, levers, operations, payout administration, governance, compliance and support delivery vehicles
• Collaborate with business teams to frame analytical questions, define hypotheses, and design experiments that inform operations enhancements and solutions
• Engage with stakeholders to review concepts for solutions using data-driven insights to guide improvements
• Translate business context into analytical frameworks to solve complex operations and performance challenges
• Design, develop, and implement scalable methods, processes, and systems to consolidate and analyze large, diverse datasets—including unstructured “big data”—to generate actionable insights for business impact
• Build and maintain data pipelines and automated processes to cleanse, integrate, and evaluate data from multiple sources, ensuring high data quality and availability
• Apply advanced statistical techniques and machine learning models (e.g., classification, regression, NLP, forecasting) to solve complex business problems and drive measurable outcomes
• Build AI agents that can deliver subject-relevant data and insights using natural language prompts, enabling intuitive access to analytics for business users
• Leverage Microsoft’s AI/ML stack (Azure Machine Learning, Azure Databricks, Azure Cognitive Services) to deploy scalable models
• Conduct scenario modeling and effective reviews of communication methods and delivery vehicles
• Evaluate performance and ensure alignment with business objectives
• Interpret and communicate insights clearly to technical and non-technical stakeholders, linking analytical findings to business objectives and recommending data-driven actions
• Act as a trusted advisor to functional team members and org leadership, providing insights that influence strategy and seller behavior
• Collaborate cross-functionally with internal and external stakeholders to define project roadmaps, evaluate model performance, and ensure continuous improvement through feedback loops
• Contribute to the development of global tools and processes for communication and readiness analytics
• Identify opportunities for automation and process optimization
• Stay current with industry trends and emerging technologies in data science and incentive design
• Assesses programs for potential risks, verifying adherence to company policies and procedures when executing compensation practices
• Identifies control measures and governance needs
• Writes efficient, readable, and extensible code and models spanning multiple features and solutions
• Contributes to code and model reviews with actionable feedback, and maintains proficient expertise in modeling, coding, and debugging techniques — including isolating and resolving errors and defects
• Leads project teams in gathering, integrating, and interpreting data from multiple sources to troubleshoot issues end-to-end
• Provides feedback to product groups on non-optimized features and explores potential for new capabilities
• Brings advanced proficiency in big-data and ML engineering tools and practices, including Hadoop, Apache Spark, CI/CD, Docker, Delta Lake, MLflow, Azure ML, and REST API development
• Generalizes ML solutions into repeatable frameworks — modules, packages, and general-purpose tools — for broader team reuse
• Enforces team standards for bias, privacy, and ethics
• Reviews teammates' model methodology and performance, recommending improvements where appropriate
• Anticipates risks such as data leakage, bias/variance tradeoffs, and methodological limitations, guiding teammates toward sound solutions
• Drives best practices in model validation, implementation, and deployment
• Develops operational models that run reliably at scale
• Partners cross-functionally to identify opportunities for ML and predictive analysis
• Uncovers new customer scenarios for transformative ML-driven solutions while incorporating AI ethics best practices
• Maintains deep, current proficiency in emerging AI/ML methodologies
• Oversees data acquisition and ensures datasets are properly formatted and accurately documented
• Uses SQL, Python, and visualization tools to explore data — analyzing distributions, attribute relationships, sub-population properties, and statistical summaries
• Builds data platforms from scratch across product lines
• Designs data-science business solutions using established technologies, patterns, and practices
• Provides guidance on operationalizing models created by data scientists
• Identifies new opportunities from data and processes it for general-purpose use
• Contributes to thought leadership and IP on data acquisition best practices
• Leads resolution of data-integrity issues
• Conducts thorough reviews of analytical techniques and processes, highlighting gaps or areas needing reexamination
• Uses assessment findings to determine next steps — deployment, further iteration, or new project directions
• Ensures clear alignment between selected models and business objectives, validating that model outputs drive meaningful outcomes
• Defines and designs feedback loops and evaluation methods to measure ongoing model impact
• Embody our culture and values

• 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

• 5+ years of hands-on experience with cloud data platforms (e.g., Azure, AWS or Google etc.)
• 5+ years of hands-on experience translating business requirements into data-driven solutions using ML algorithms (e.g., classification, regression, clustering, NLP etc.)
• 2+ years of experience in PowerBI reporting and SSAS
• 2+ years of experience in business planning
• Hands-on experience with cloud platforms and tools such as Azure Foundry, with a focus on developing and deploying AI models
• Experience designing, building, or deploying agentic AI systems — including autonomous agents, multi-agent orchestration, tool-use frameworks, or agent-based workflows using platforms such as LangChain, AutoGen, Semantic Kernel, or similar

Certain roles may be eligible for benefits and other compensation