AI Skilling Data Analyst

We are seeking a highly analytical and collaborative AI Skilling Data Analyst to join our team. In this role, you’ll help shape the future of workforce development by leveraging data to evaluate, optimize, and personalize AI-powered skilling programs. Your insights will directly inform how employees learn, grow, and thrive in a rapidly evolving digital landscape. 

The Global Learning and Skilling team focuses on enhancing employee skilling through AI-driven, personalized experiences. By combining various learning data with human motivation principles, the team aims to create customized skilling opportunities integrated in the flow of work. Preparing data for AI processing is fundamental to this process, as well as converting skilling data into strategic insights.  

In this role, you will work closely with Human Resources (HR), Engineering, External Staff, and business teams to utilize AI in identifying key data sets and translating insights into actionable recommendations. You will provide information on skilling ROI (return on investment), workforce capabilities, skilling optimization, and compliance trends. 

Join a team that’s redefining how people learn and grow. You’ll help shape intelligent skilling ecosystems that empower employees and prepare organizations for the future of work. If you’re passionate about AI, learning, and data—and love making impact through insights—this role is for you. 

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. 

Responsibilities

Key Responsibilities

• Design and evolve Power BI / Microsoft Fabric dashboards that track AI-generated learning engagement, skill progression, and outcomes. 
• Apply machine learning models (from internal or vendor tools) to identify learning effectiveness, performance risk, and recommended next actions. 
• Partner with business leaders to frame data stories that influence decisions on skilling, hiring, and capability building. 
• Champion the integration of predictive analytics to forecast workforce readiness and business alignment. 

Data Analysis & Reporting

• Analyze usage, performance, and impact of AI-driven learning experiences to measure skill acquisition, program efficacy, and learner engagement. 

Skills Intelligence

• Partner with internal teams to build and maintain dashboards that visualize skilling progress, competency gaps, and upskilling opportunities using structured and unstructured data. 

AI & Learning Systems Integration

• Collaborate with product, engineering, and instructional design teams to embed intelligent insights into learning platforms (e.g., personalized nudges, adaptive pathways). 

Model Evaluation & Feedback Loops

• Assess the accuracy and relevance of AI-generated recommendations and content to improve skilling effectiveness over time. 

Data Governance & Ethics

• Ensure all analyses align with data privacy policies, responsible AI principles, and compliance standar