AI Data Strategy Lead
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Translate AI capability goals into data requirements and readiness
- Build high-quality data pipelines, eval sets and learning flywheels
- Govern AI data and allocate investment across the AI portfolio
Benchmark: OpenAI Human Data / Data Understanding + Anthropic Human Data / feedback-loop roles (Sep 2026): translate research and product capability goals into acquisition, labeling, curation, retrieval and eval-data requirements; define quality and coverage metrics; build training, RAG and eval flywheels with provenance, rights and privacy; connect research roadmaps to scalable, cost-aware data operations. Mastery ladder: 2 job-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
Who’s Hiring Right Now
US openings posted in the last 7 days at Anthropic, OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Translate AI capability goals into data requirements and readiness
AI use-case readiness, capability gaps + data-value priorities
Coverage: AI Strategy / Data Strategy / Product Discovery / KPIs
Training, retrieval and evaluation data architecture
Coverage: Training Data / RAG / Vector Databases / AI Evals
Build high-quality data pipelines, eval sets and learning flywheels
Data curation, synthetic data, quality + coverage
Coverage: Data-Centric AI / Synthetic Data / Curation / Quality
Evaluation datasets, representativeness + production feedback loops
Coverage: AI Evals / Experimentation / Monitoring / Human Feedback
Govern AI data and allocate investment across the AI portfolio
AI data governance, lineage, rights + responsible use
Coverage: Data Governance / Responsible AI / Privacy / Auditability
Data flywheels, economics + roadmap / portfolio decisions
Coverage: AI Product Strategy / Data Economics / Cost / Executive Leadership
Role-Readiness Proof / Capstone
Build a 12-18 month AI data strategy across a real portfolio: capability map, data-readiness assessment, source/rights inventory, training/RAG/eval architecture, curation and synthetic-data pipeline, quality/coverage thresholds, eval suite, feedback flywheel, lineage/provenance, sensitive-data controls, build-buy-license decisions, cost assumptions, operating model and outcome roadmap.
Proof Rubric
- Connect AI goals to explicit data requirements
- distinguish training, retrieval and evaluation data
- prove quality and coverage
- handle rights, privacy and provenance
- design a closed learning loop
- quantify cost and value
- make governance enforceable
- prioritize the roadmap from capability evidence rather than AI hype