Sales Compensation Insights Lead

Overview

The Sales Compensation Insights Lead applies in-depth knowledge of the business, the evolving data landscape, tools, and technologies, and the lineage of those data across multiple areas. Applies a customer- and/or stakeholder-oriented focus by understanding their needs, perspectives, and how they leverage data insights/tools. 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. Applies expertise in data, business, and customer needs to evaluate and determine ideal analytical and statistical techniques to address business and/or research questions. Shares insights and analytical expertise to tell stories of analyses through one or more means. Builds, supports, and/or consults others on the execution of formal experiments or prototypes/proofs of concepts. 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. 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.

Responsibilities

Sales Design and Incentives specific:

• Support design and analysis of incentive compensation plans, aligning them with Microsoft's go-to-market strategies and business objectives.
• 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.
  
  

Business and Data Landscape

Applies in-depth knowledge of the business, the evolving data landscape, tools, and technologies, and the lineage of those data across multiple areas. Links business topics to relevant data sources and external trends, anticipates data and business requirements, and probes for further insights into relevant business- or data-related topics to support data sourcing and integration decisions. Proactively anticipates business questions, develops data frames and analytical solutions, builds connections across business areas, and identifies and acts on opportunities to develop existing, enhanced, or automated data infrastructure, analyses, and solutions that enable the evaluation of business questions. May coach others to help them broaden their knowledge of the business and relevant data sources. Remains up-to-date on latest tools, technologies, best practices in data analytics, and changing regulations.

Customer/Stakeholder Orientation

Applies a customer- and/or stakeholder-oriented focus by understanding their needs, perspectives, and how they leverage data insights/tools. Validates and advises customer and/or stakeholder requirements, focusing on broader customer organization/context and enables customer adoption by delivering accessible solutions and supporting relationships. Works with customers and/or stakeholders to overcome obstacles, develop tailored and practical solutions, and ensure proper execution. Builds trust with customers and/or stakeholders by leveraging the knowledge of Microsoft products and solutions, interpreting data within relevant contexts, and articulating key details to drive realistic customer expectations and an understanding of the limitations of their data.

Data Analysis

Applies expertise in data, business, and customer needs to evaluate and determine ideal analytical and statistical techniques to address business and/or research questions. Guides and establishes partnerships with others to execute complex analyses, resolve analytical challenges, interpret results across relevant contexts, and provide actionable recommendations. Critically evaluates the choice of tools, techniques, and assumptions to highlight potential gaps and ensure they are utilized appropriately within context, that outcomes align with business and/or research needs, and provides feedback on features and functions of analytical tools and/or models. Anticipates the risks of data leakage, analytical tradeoffs, methodological limitations, etc., and can guide teammates on solutions.

Data Model Evaluation

Understands relationship between analytical model(s) and business objectives. 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. Coaches and mentors less experienced analysts as needed. Presents results and findings to senior stakeholders.

Data Privacy and Governance

Maintains expertise in data privacy and security requirements, responsible and ethical data handling and AI practices, and models compliance with classification and governance rules and regulations. 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. Enforces team standards related to bias, privacy, security, and ethics related to data usage and handling as needed. Knows where to seek expertise on data privacy and security rules and regulations and shares personal knowledge of them with peers as needed to exemplify and enforce standards related to bias, privacy, security, and ethics. Identifies and addresses impact of updated guidance on work activities and results.

Experimentation and Innovation

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. Partners cross-functionally to advise on experimental design or evaluation frameworks for established and/or emerging data sources, as well as decisions related to data use, personalization, and to ensure inferences are appropriate to the data and design when interpreting results. Assesses and takes calculated risks, and applies previous learnings to influence mitigation plans. Synthesizes and connects results across experiments, identifies relevant connections to other work, and makes data-driven recommendations for next steps with clear links to strategic business goals. Determines and recommends optimal and innovative methods, tools, and technologies for operationalizing, sharing, and scaling experimental insights and/or expedite the process.

Expertise in Data

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).

Qualifications

Required/minimum qualifications

Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR equivalent experience.

Additional Or Preferred Qualifications

Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 8+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR equivalent experience.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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