Performance Marketing Lead

Core Responsibilities

• Own customer paid onboarding end-to-end: connect ad accounts, MMPs (AppsFlyer, Adjust, Branch), GA4, and attribution stacks to Hilbert; turn messy measurement configurations into clean, consistent signal.
• Work directly with customers on toughest paid media problems (cohort analysis, LTV interventions, cross-channel reallocation) and own outcomes alongside them.
• Drive Hilbert's channel activation layer: define and ship use cases for sending model outputs (pLTV, churn risk, propensity scores) back into Meta, Google, and other platforms as conversion events, value signals, and audiences; work with engineering on integrations (Conversions API, offline events, tool-based connections).
• Set paid media strategy for customer accounts: turn Hilbert's predictive outputs into concrete budget allocation and growth interventions across acquisition, retention, and monetization.
• Build Hilbert's internal performance marketing playbooks and systems; frameworks for operating across Meta, Google, TikTok, Snapchat, Pinterest, programmatic, and beyond.

Requirements

• Genuine accountability for paid performance across the entire ecosystem, not just one channel; experience owning outcomes where decisions directly moved results.
• Owned paid growth outcomes with strategy and budget decisions while staying hands-on with mechanics.
• Managed integrated, multi-channel paid programs across Meta, Google, TikTok, Snapchat, and other platforms simultaneously.
• Deep understanding of modern measurement: MMPs, attribution modeling, SKAdNetwork, incrementality testing, extracting reliable signals from noisy ecosystems.
• Technical enough to build: implemented CAPI, offline conversion imports, server-side tracking, or MMP integrations; comfortable reading API documentation.
• Understanding of why optimizing on predicted LTV outperforms raw purchase events; excited to build feedback loops.
• Fluent in GA4, platform-native analytics, and attribution tools; able to combine multiple imperfect data sources.
• Experience inheriting and turning around unsolved growth problems.
• Approach growth through experimentation; let data reveal structure; excited to work alongside AI systems.
• Explain failures with same rigor as successes; focus on understanding why things happened.
• Belief that current performance marketing approaches are broken and desire to build what comes next.

Nice-to-Haves

• Experience building performance marketing frameworks and systems.
• Hands-on work with predictive model outputs (pLTV, churn risk, propensity scores) in ad platforms.
• Background in turning AI/ML outputs into actionable paid media decisions.