Marketing Analytics Manager - AI Model Training - Remote

Job Type: ContractorLocation: RemoteRole DescriptionIf you’re a senior marketing analytics professional who thrives on measurement rigor, attribution nuance, experimentation, and data-driven growth strategy, this is a unique opportunity to contribute directly to how the next generation of AI systems reason about marketing performance. You’ll challenge and evaluate advanced language models on domain-specific topics such as multi-touch attribution, paid media performance, cohort analysis, and conversion rate optimization to strengthen model reasoning and improve how AI systems communicate marketing insights.Your Profile4+ years of professional experience in marketing analytics, growth analytics, marketing science, revenue analytics, or a closely related field.Significant hands-on work with marketing performance data, including paid media, web analytics, CRM, lifecycle marketing, campaign reporting, experimentation, or customer funnel analysis.Deep knowledge of attribution modeling, CAC/LTV analysis, funnel optimization, customer segmentation, marketing KPIs, and performance measurement frameworks.Strong understanding of statistical reasoning, A/B testing, incrementality, cohort analysis, data visualization, and the limitations of common marketing measurement approaches.Proven experience translating complex marketing data into clear business recommendations for executives, marketing leaders, product teams, or growth stakeholders.Bachelor’s degree in marketing, business analytics, statistics, economics, data science, mathematics, or a related field; a master’s degree or MBA is preferred.Key ResponsibilitiesEvaluate AI-generated responses for accuracy, clarity, analytical rigor, and practical usefulness in marketing analytics contexts.Challenge advanced language models with complex marketing analytics scenarios, including attribution tradeoffs, funnel diagnostics, campaign performance analysis, and growth forecasting.Review and refine AI-generated prompts, model responses, reasoning chains, and explanations related to marketing data, metrics, and decision-making.Identify errors, omissions, misleading assumptions, or weak analytical logic in AI-generated marketing recommendations.Provide structured feedback that helps improve model behavior, domain accuracy, and the quality of AI-supported marketing analysis.Shape AI communication standards for how models explain dashboards, statistical findings, experiment results, campaign outcomes, and business implications.Support benchmarking efforts by creating and evaluating tasks that test model performance across real-world marketing analytics use cases.Contribute domain expertise to improve how AI systems handle executive summaries, performance narratives, and actionable marketing recommendations.