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# Data Partnerships Lead

- **Company:** Tencent America
- **Location:** Los Angeles, CA, United States
- **Experience:** Expert
- **Salary:** $131,000.0 - $246,800.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Data Governance, Open Source Technology, DataOps, Technical Data Management Systems, Data Processing, Data Ingestion, Data Strategy, Data Pipelines
- **Published:** June 27, 2026
- **Apply:** https://www.dice.com/job-detail/064de877-291a-4806-be8f-dbab650978fa

## About the Role

* Demonstrated expertise in high-leverage technical environments, with a strong background in Technical Program Management (TPM), AI Data Operations, Strategic Tech Deals, Data Licensing, or Management Consulting.
 * Optimize Data Ingestion Workflows: Standardize external dataset specifications and integrate them into internal data processing and pre-training workflows to realize scalable asset accumulation.
 * Deep operational knowledge of North American data commercial compliance rules and IP licensing rchitectures.

## Description

We are seeking a Data Partnership Lead to drive data operations and strategies for our frontier AI research initiatives. In this role, you will design end-to-end data acquisition strategies, manage key vendor relationships, and own the data pipeline from requirements to production. This role requires a unique blend of technical depth-to evaluate what constitutes high-quality training data-and operational excellence to execute at a global scale., * Execute End-to-End Data Strategy: Partner directly with foundational model research and algorithm leaders to identify technical data gaps, translating research roadmaps into targeted global sourcing and supplementation strategies.

 * Drive Strategic Partnerships: Own the full lifecycle of high-value technical data acquisition across North American commercial vendors, research networks, and open-source communities-from initial sourcing and executive pitching to structured commercial negotiation.
 * Establish Quality & Evaluation Metrics: Collaborate with engineering teams to build data evaluation standards, assessing the technical quality, scarcity, and training utility of external assets to ensure they meet strict algorithmic benchmarks.
 * Navigate Complex Data Governance: Work cross-functionally with Legal, Privacy, and Security teams to structure licensing, provenance, and anonymization terms (including CCPA/CPRA and broader data regulations) that guarantee legal training and commercial usability
 * Scale Data Operations: Build robust, long-term vendor and partner ecosystems, managing delivery SLAs and pipeline tracking to accelerate research velocity without sacrificing quality.


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