360 Social Agency

Performance Marketing

• Lead the Paid Media budget plan and allocation strategy across products, geos, languages, and channels, optimizing for Return on Investment and Lifetime Value
• Own end-to-end campaign strategy and execution across multiple platforms: Google Search, DV360, PMax, Meta, UAC, and ASA, among others. From strategy and setup to bid management and creative testing programs.
• Own audience strategy across the funnel, including segmentation, remarketing, and lookalike audiences, to maximize reach efficiency and conversion
• Define the measurement framework: own attribution infrastructure, incrementality testing, and Marketing Mix Modeling (MMM) to ensure investment decisions are grounded in causal evidence. Work in close collaboration with the Data and Engineering team.
• Manage relationships with agencies, platform partnerships, and vendor agreements, including negotiations, renewals, and performance accountability
• Build, develop, and mentor the paid media team, set clear goals, improve ways of working, and create conditions for high performance
• Deliver regular performance reporting tied to business outcomes, CAC, ROAS, LTV, CPI, with clear recommendations for leadership and cross-functional stakeholders
• Continuously evaluate and pilot emerging ad formats, AI-powered campaign types (incl. AI Max), and platform innovations to keep the paid media function ahead of market shifts
• Articulate the investment plan and performance results in structured presentation and reporting formats
• Run structured A/B tests across ads, landing pages, and bidding strategies; translate results into scalable improvements
• Build and manage a global affiliate program, designing commission models that drive incremental sales
• Partner with Data, Product, Finance, and Legal to align paid media investments with broader growth and profitability goals

Manager, Paid Media

AI Engineer

• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems.
• Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry.
• Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts.
• Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated.
• Optimize existing models for better performance and throughput.
• Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing.
• Build and maintain tools for deployment, monitoring, and operations. - Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.

Manager, Paid Media

Applied AI Engineer – Level II