Principal Product Manager for Microsoft's AI Data Platform
The Principal Product Manager for Microsoft's AI Data Platform is a strategic leader responsible for driving the vision, execution, and impact of a central platform that manages the full lifecycle of first-party, third-party, synthetic, and human-labeled data for AI training across Microsoft. This role is mission-critical to breaking down data silos, maximizing ROI on data investments, and accelerating AI model development. At Microsoft, our mission is to empower every developer and organization to achieve more with AI. As part of the CoreAI organization, the Data for AI team sits at the heart of this mission : we design and operate Microsoft's central AI data platformthe backbone for acquiring, curating, synthesizing, and governing the data that powers our most advanced language and multimodal models.
As a Principal Product Manager for Data for AI, you will define and drive the vision for the data lifecycle spanning first-party, third-party, synthetic, and human-labeled data. You will lead a team of PMs and partner deeply with engineering, research, legal, compliance, and business stakeholders to make high-value datasets discoverable, reusable, cost-efficient, and compliant. This role is critical to maximizing ROI on Microsoft's data investments, accelerating model innovation, and ensuring responsible AI at scale.
We are looking for a proven AI / ML product leader with startup scrappiness and Big Tech scale experience - someone who thrives in ambiguity, has a track record of execution, and can unify a complex ecosystem into a coherent platform strategy.
Success in Role
In your first 6-12 months, success looks like :
Responsibilities
Platform Strategy & Execution : Define and drive the vision, strategy, development and roadmap for Microsoft's cross-company AI data platform to making data discoverable, accessible, and reusable across teams.
Lifecycle Automation : Drive automation of dataset ingestion, registration, validation, PII curation, discovery, sharing, and lineage.
Security & Compliance : Strengthen security, manage capacity and entitlements, and integrate with governance and compliance services.
Stakeholder Engagement : Collaborate to align priorities and deliver high-impact solutions.
Data Acquisition & Curation : Strategically acquire, generate, and onboard high-value datasets, minimizing redundancy and optimizing costs.
Customer-Driven Roadmap : Establish a customer-driven priority framework and long-term roadmap for platform evolution.
Continuous Improvement : Implement feedback loops to improve data quality and model performance, leveraging human-in-the-loop workflows for annotation and validation.
Operate engineering tooling : oversee automation pipelines and continuous improvement of data-onboarding workflows.
Collaborate with product teams : engineers, and researchers across Microsoft and external vendors to identify gaps and drive timelines toward resolution.
Qualifications
Required Qualifications :
Other Requirements :
Preferred Qualifications
Bachelor's Degree AND 10+ years experience in product / technical program management, data analysis, or product development OR equivalent experience.
10+ years experience managing cross-functional and / or cross-team projects.
Deep expertise in AI / ML data pipelinespre-training, fine-tuning, evaluation, and deployment.
Track record of leading cross-functional efforts at scale in high-stakes, fast-paced environments.
Startup founder / early-stage experience.
Demonstrated expertise in Azure logging / monitoring, private networking (Private Endpoints / Links), and large-scale data-ingestion pipelines.
Proven experience in product management for large-scale data or AI platforms.
Proven technical background in data lifecycle management, security, and compliance.
Demonstrated ability to lead cross-functional teams and drive strategic initiatives.
Excellent communication and stakeholder management skills.
Experience with cloud data platforms (Azure, AWS, GCP).
Entrepreneurial mindset; thrives in fast-paced, ambiguous environments.
Experience with automation, data pipelines, and human-in-the-loop systems.
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