Jobs at Spotter

Leads Spotter’s ad sales marketing strategy, positioning, RFPs, proposals, creator-led brand partnerships, sales materials, and campaign execution. Coordinates Sales, Research, Planning, Product, Operations, creators, agencies, clients, and legal partners from pitch development through post-campaign storytelling and renewals. The role develops advertiser solutions, measurement frameworks, integrated packages, go-to-market messaging, and scalable processes to improve sales effectiveness and campaign outcomes.

Leads ad sales marketing strategy, RFPs, proposals, positioning, creator-led brand partnerships, and campaign execution. Develops sales materials, integrated advertising solutions, measurement frameworks, and post-campaign renewal narratives. Coordinates Sales, Research, Planning, Creator Partnerships, Product, Operations, clients, agencies, creators, production partners, and legal teams to deliver compelling, commercially aligned campaigns. Improves go-to-market messaging, sales processes, packaging, and campaign storytelling across Spotter’s creator-focused advertising business.

Analyze pricing, campaign, and portfolio performance to inform Ad Sales and Capital business decisions. Build dashboards, reports, and analytical tools; identify trends and optimization opportunities; automate recurring processes; monitor campaign and portfolio results; and translate complex data into actionable recommendations. Collaborate with Creator, Ad Sales, Finance, Engineering, and Analytics teams to develop scalable, data-driven solutions.

Lead analytics engineering strategy and mentor analytics engineers while delivering scalable data pipelines, ETL processes, analytics-ready datasets, data quality frameworks, dashboards, and self-service reporting tools. Partner with data engineers, data scientists, analysts, and senior stakeholders to translate business needs into reliable technical solutions using Python, SQL, dbt, data warehouses, Spotfire, and Power BI.

Design, train, evaluate, optimize, and deploy production ML models including recommendation, ranking, personalization, and adaptive decision systems. Apply reinforcement learning, contextual bandits, offline policy evaluation, causal inference, and experimentation to improve creator-facing products. Build scalable training, evaluation, deployment, and inference pipelines; work with logged interaction data; collaborate with Product, Engineering, and Analytics to translate customer problems into production ML solutions.