OverviewThis role blends deep technical expertise in data science with business acumen in sales compensation, operations, payout administration, governance, compliance, and support. You will partner with cross-functional teams to design solutions to drive efficiency, build machine learning solutions and AI agents, and deliver insights that influence operations and delivery of vehicles. In addition, you'll engage with diverse stakeholders, manage complex relationships, and drive innovation across the organization.
We are looking for a
Senior Data Scientist who is willing to work in a dynamic environment to solve real life day-to-day problems, leveraging data science techniques. You will enjoy and be successful in this role if you are curious and willing to challenge the status quo and come up with data-driven solutions to ambiguous problems.
As a Senior Data Scientist, you will focus on continuously improving operations to enable strategy, striking the right balance between pushing the envelope to innovate and ensuring that innovations can be implemented to deliver a great seller experience. Your work will directly influence product direction, customer success motions, and executive decision-making.
ResponsibilitiesThe
Senior Data Scientist is responsible for the following:
Business Management:- 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.
Business Understanding & Impact :- Demonstrate strong 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.
Data Science :- 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.
Analytics & Evaluation: - 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.
Stakeholder Engagement:- 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.
Innovation & Continuous Improvement: - 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.
Governance and Compliance:- Assesses programs for potential risks, verifying adherence to company policies and procedures when executing compensation practices.
- Identifies control measures and governance needs.
Coding and Debugging:- Writes efficient, readable, and extensible code and models spanning multiple features and solutions. Contributes to code and model reviews with actionable feedback, and maintains strong 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 expert-level 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.
Modeling and Statistical Analysis:
- 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 on scale.
- Partners cross-functionally 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 expertise in emerging AI/ML methodologies.
Data Preparation and Understanding:
- 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.
Evaluating for Insights and Impact: - 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.
Other- Embody our culture and values.
QualificationsMinimum Qualifications - 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 Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience
- OR 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 equivalent experience.
Preferred Qualifications:- 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 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 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 equivalent experience.
- 5+ years of hands-on experience with cloud data platforms (e.g., Azure, AWS or Google etc.) required.
- 5+ years of programming experience in Python, SQL Server, and PySpark, including understanding and maintaining scalable data pipelines and machine learning models.
- 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+ year of experience in PowerBI reporting and SSAS is a plus
- 2+ year of experience in business planning is plus.
- Strong communication skills and ability to collaborate across cross-functional teams. Experience managing stakeholder and leader communications effectively
- Experience in quota modeling, incentive compensation, or sales analytics and forecast is a plus.
- Proven ability to mentor junior data scientists and lead end-to-end ML lifecycle projects.
- Hands-on experience with cloud platforms and tools such as Azure Synapse and 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.
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
