Sales Compensation Insights Lead

## About the role Builds, supports, and/or consults others on the execution of formal experiments or prototypes/proofs of concepts. Sales Design and Incentives specific: Conduct advanced modeling, scenario analysis, and performance reviews to evaluate plan effectiveness and provide actionable insights. Build and maintain robust data pipelines and dashboards to support compensation analytics and reporting needs. Collaborate with cross-functional teams (e.g., Finance, HR, Sales, Engineering) to ensure compensation strategies are fair, compliant, and optimized for business impact. Act as a trusted advisor to stakeholders, providing consultation on compensation design, governance, and risk mitigation. Drive continuous improvement by contributing to global tool development, user acceptance testing (UAT), and process innovation. Ensure adherence to governance standards and compliance with internal policies and external regulations. Works with customers and/or stakeholders to overcome obstacles, develop tailored and practical solutions, and ensure proper execution. Guides and establishes partnerships with others to execute complex analyses, resolve analytical challenges, interpret results across relevant contexts, and provide actionable recommendations. Establishes clear linkage between generated data models and desired business objectives to assesses the degree to which data models meet business objectives and highlights gaps or areas that have been missed. Ensures alignment on definitions and standards across stakeholders and defines, designs, and promotes the use of appropriate feedback and evaluation methods. Ensures data have undergone appropriate Corporate, Executive, and Legal Affairs (CELA) reviews and ensures work activities and results are in alignment with principles and controls. Builds, supports, and/or consults others on the execution of formal experiments or prototypes/proofs of concepts, to evaluate the impact of new or changed features or processes. Applies expertise in data sources, formats, and quality to identify and leverage data across multiple sources, understands data requirements, and evaluates the sufficiency of data for addressing relevant and impactful business questions. Determines and leverages optimal methods and tools for integrating data and proactively works to identify and address data integrity, quality, and/or access issues. Recommends opportunities to build new data pipelines or integrations to better meet requirements, and initiates collaborative action to source additional data. Develops and/or recommends initial/prototype data models and/or tools for others' consumption, leverages relevant data and frameworks from other teams, and escalates complex issues with data or data models to appropriate Engineering or Data-Science teams. ## Improvement and Efficiency Identifies and promotes methods that create efficiency in core work related to analytics and reporting that are reusable, readily discoverable by decision makers, self-service, and directed to meaningful interpretation of data and driving business decisions. Recommends and socializes optimal methods for operationalizing, sharing, and scaling insights, shares expertise and a practical rationale for when ad-hoc analyses should become part of regular reporting features. Shares critical domain expertise to create clarity, ensure readiness to appropriately consume and leverage data and/or insights, and evaluate the viability of automated methods for use in data collection, reporting, and/or analysis. Participates in the peer review process and auditing of others' work to ensure quality and relevance of analyses and validate insights. ## Orchestration and Collaboration Leverages working relationships within and across teams to ensure alignment and quality execution of data sourcing, methods, model development and application, and the appropriate use of analytical tools and processes. Works with internal stakeholders to identify and promote the adoption of recommended data sources and analysis practices to address business priorities and deliver key insights and results. Seeks opportunities to develop and leverage expertise to identify areas for innovation to address use cases and/or evolving business needs. Proactively engages stakeholders to identify and act on opportunities to leverage data, resources, and solutions that were instrumental to success in similar contexts and consults across teams (e.g., vendors) on decisions related to data sourcing, analyses, and the interpretation of analytical results. ## Reporting and Sharing Results Shares insights and analytical expertise to tell stories of analyses through one or more means, including dashboards, reports, data visualizations, interactive self-service platforms, slides, internal forums, ad-hoc inquiries, and talking points that highlight relevant insights. Synthesizes and simplifies details across analyses and reporting platforms to highlight the most relevant findings that can help inform business decisions and identifies opportunities to improve the efficiency of insights reporting techniques. Guides others and establishes partnerships with stakeholders to ensure results are accessible, and can provide information accurately, clearly, and with sufficient relevance to influence decision making for intended audience(s). ## Applying Data Management Principles Ensures data used in solutions is accurate, available, secure and complete. Engages in ongoing improvements to optimize operational efficiency. ## Artificial Intelligence (AI) Ethics Knowledge of issues around fairness, transparency, accountability, and ethics in relation to the use of artificial intelligence (AI). ## Data Cleaning The ability to detect and correct corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data and then replacing, modifying, or deleting as needed to maintain the integrity of the data. ## Data Integration Knowledge of technical and business processes used to combine data from disparate sources into meaningful and valuable information to deliver a complete data integration solution. ## Data Integrity The ability to maintain and assure the accuracy and consistency of data over its entire lifecycle in design, implementation, and usage of systems. This includes knowledge of data governance practices to ensure data quality and defined risk management surrounding the handling of data. ## Data Mining The ability to examine large databases to discover patterns and generate new information. This includes the use of machine learning, statistics, and database systems and aims toward extracting information from a data set and transforming the information into a comprehensive structure for further use. ## Data Modeling Knowledge of data modeling procedures and the ability to identify and clearly define the business data entities (for example, persons, places, things, concepts, events), attributes, and relationships between them. This includes the ability to read, understand, and develop test data models. ## Communicating and Influencing * **Collaboratively:** Articulates ideas clearly and engages with stakeholders convincingly. Adapts their communication style to different audiences, ensuring messages are both understood and persuasive. * **Conflict Resolution:** The ability to manage conflict, disharmony, and strife among people and situations, while recognizing and addressing sensitivities. * **Coordination Ability:** to organize the different elements of a complex activity so as to enable them to work together effectively. * **Influence Others:** The ability to garner support for initiatives by gaining the respect of others and inspiring trust and confidence. * **Negotiation:** The ability to achieve mutually satisfying agreements in negotiations with others by listening to their objectives, acting as the company's representative to effectively communicate the company's objective, and seeking common ground and collaborative