Data Scientist II
 Microsoft | |
 United States, Washington, Redmond | |
 Sep 21, 2025 | |
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OverviewMicrosoft is a company where passionate innovators come to collaborate, envision what can be and take their careers to levels they cannot achieve anywhere else. This is a world of more possibilities, more innovation, more openness in a cloud-enabled world.The Business & Industry Copilots group is a rapidly growing organization that is responsible for the Microsoft Dynamics 365 suite of products, Power Apps, Power Automate, Dataverse, AI Builder, Microsoft Industry Solution and more. Microsoft is considered one of the leaders in Software as a Service in the world of business applications and this organization is at the heart of how business applications are designed and delivered. As a Data Scientist II on the Customer Experience team, you will play a pivotal role in advancing Microsoft's AI transformation by working directly with engineering teams to develop hyperscale solutions that support sales, marketing, and support platforms. In this position, you will utilize advanced analytics, statistical modeling, machine learning, and generative AI techniques to extract insights, drive impactful actions, and address complex business challenges. This role enables you to collaborate with a diverse group of data scientists, engineers, and product managers, deepen your expertise in the rapidly evolving fields of AI and machine learning, and make a meaningful difference for thousands of users who rely on Microsoft's business platforms. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
ResponsibilitiesWork with key stakeholders to understand the underlying business needs and formulate the needs into discrete, manageable problems with well-defined measurable objectives and outcomes. Transform formulated problems into implementation plans by defining success metrics, applying/creating the appropriate methods, algorithms, and tools, as well as delivering statistically valid and reliable results. Write robust, reusable, and extensible code to support analysis and modeling. Develop new ML or GenAI based models using advanced statistical and ML techniques. Lead the evaluation of various GenAI based solutions, diagnosing issues and identifying root causes to support potential fine-tuning or reinforcement learning based fixes.Use AI-powered tools in your daily work to accelerate coding, analysis, and other tasks. | |