Senior Data Scientist

Business Management: 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. Collaborate with business teams to frame analytical questions, define hypotheses, and design experiments that inform operations enhancements and solutions. 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. Evaluate performance and ensure alignment with business objectives. 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. Assesses programs for potential risks, verifying adherence to company policies and procedures when executing compensation practices. Identifies control measures and governance needs. 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. Provides feedback to product groups on non-optimized features and explores potential for new capabilities. Develops operational models that run reliably at scale. 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. Contributes to thought leadership and IP on data acquisition best practices. Ensures clear alignment between selected models and business objectives, validating that model outputs drive meaningful outcomes. 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) 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 5+ 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 7+ 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.) required. 5+ years of hands-on experience translating business requirements into data-driven solutions using ML algorithms (e.g., classification, regression, clustering, NLP etc.) required. 2+ years of experience in PowerBI reporting and SSAS is a plus 2+ years of experience in business planning is plus. Effective communication skills and ability to collaborate across cross-functional teams. Experience managing stakeholder and leader communications effectively. Hands-on experience with cloud platforms and tools such as Azure Foundry, with a focus on developing and deploying AI models is a plus. 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 is a plus.