Machine Learning Data Scientist

About the position

The Worldwide Incentive Compensation (WWIC) team's mission is to enable strategy, motivate sellers and reward results. In pursuit of this mission, the team designs and implements variable incentive-based compensation plans for more than 30,000 sellers, sales leaders and consultants across Microsoft. We are the experts in Incentive Compensation at Microsoft that are responsible for aligning incentive plans and targets (quotas) to motivate seller behavior and drive the company's strategies. We are also the end-to-end owners of incentive compensation processes and data which includes quota setting management, delivering timely and accurate pay-outs. The Machine Learning Data Scientist will collaborate with key stakeholders across Business Operations & Sales Excellence, Finance, Mint Engineering, Finance Data & Experiences (FD&E), Plan Design, analytics and the broader WWIC organization. This role will lead the development of an AI agent to centrally manage budget restatements and proactively identify opportunities for quota adjustments based on both role-specific and overall business performance and forecasts. The ideal candidate should be detail-oriented, able to translate business context and requirements into quota modeling. Excels with machine learning algorithms, statistical analysis, forecast modeling and capable of deploying models in Azure. Ability to digest complex problems, formulate creative solutions and successfully land those solutions with business partners. This individual should have attention to detail and ability to make practical decisions in times of ambiguity. The candidate must also have proven ability to work effectively in a large, cross-geography, cross-business virtual team environment. Success for this role requires the individual to be proficient in communication, both written and verbal, and must be able to support answering questions from business partners.

Responsibilities

• Demonstrate good business acumen, work with business partners to understand data and define and solve business problems and primary objectives.
• Understand data pipelines, process flow and reports.
• Acquire data necessary for successful completion of the project plan. Proactively detect changes and communicate to senior leaders.
• Leverage AI tools and data - such as quota baselines, territory assignment, and compensation plans - to help the field better understand quota-setting mechanics and build greater confidence in the process.
• Leverage knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, NLP, image recognition, etc.) and individual algorithms to identify the best approach to set quota.
• Effectively communicate with diverse audiences on data quality issues and initiatives.
• Develop a strong understanding of the Microsoft toolset in artificial intelligence (AI) and machine learning (ML) (e.g., Azure Machine Learning, Azure Cognitive Services, Azure Databricks).
• Understand the relationship between selected models and business objectives. Ensure clear linkage between selected models and desired business objectives.
• Define and design feedback and evaluation methods.
• Use business knowledge and technical expertise to provide feedback to the engineering team to identify potential future business opportunities.
• Collaborate with end customer and Microsoft internal cross-functional stakeholders to understand business needs. Formulate a roadmap of project activity that leads to measurable improvement in business performance metrics over time.
• Apply a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context.
• Promote and ensure customer adoption by delivering model solutions and supporting relationships.

Requirements

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field.
• OR Master's Degree 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 consulting experience.
• OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results).
• OR equivalent experience.
• 3+ years of experience working with cloud tools and platforms such as Azure Synapse and Azure ML Studio, specializing in the development and deployment of AI models.
• 1+ year of experience working with Agentic AI & Language Learning Models including hands-on experience with LangChain or Auto-GPT.

Benefits

• Base pay range for this role across the U.S. is USD $100,600 - $199,000 per year.
• 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 $131,400 - $215,400 per year.