Top Remote Artificial Intelligence Data & Analytics Jobs

Turn channel data into actionable insights to improve partner behavior and forecasting. Analyze pipeline, deal quality, product mix, program ROI, and sales velocity. Deliver decision-ready recommendations, build dashboards, define data needs, and partner cross-functionally with Sales, Programs, and Ops to drive scalable revenue outcomes.

Perform part-time quality audits and evaluations of task outputs, define and refine quality benchmarks and scoring rubrics, analyze audit results to produce actionable insights and reports, and recommend process improvements to raise output quality.

Lead client-facing analytics engagements: translate ambiguous business questions into data models, dashboards, and KPIs; write production-quality SQL and dbt models; build BI reporting; apply AI tools to accelerate analysis; drive proactive insights; set stakeholder cadence; and mentor junior analysts to ensure adoption and delivery.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

Manage ingestion, processing, QC, and production workflows for litigation cases. Diagnose and resolve complex eDiscovery data issues, document chain of custody, advise on data strategy, and partner with Product and Engineering to improve the platform.

The Research Engineer will advance decentralized AI by optimizing distributed training, developing open-source tools, publishing research, and enhancing platform capabilities.

Analyze product performance metrics and user behavior to drive insights for product improvements. Collaborate with teams to develop reporting practices, A/B tests, and dashboards to inform strategy.

Participants record 1,000 short ASL or BASL videos to aid AI understanding of signed requests, working independently while following specific instructions.

Join Kled to help build the future of human data collection and design systems that influence AI data training through a consumer application.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.