Responsibilities

• Own marketing measurement and attribution frameworks across paid channels (e.g., Google, Meta, TikTok, TV), including experimentation, incrementality testing, and attribution modeling to quantify channel effectiveness and guide budget allocation
• Build and operationalize scalable measurement solutions such as Marketing Mix Models (MMM), channel-level performance diagnostics, and CAC/LTV forecasting to support growth decisions and optimize marketing spend
• Design and execute 5–6 incrementality tests annually, including power analysis, monitoring, and executive readouts
• Reconcile platform-reported metrics with internal attribution models and MMM (Northbeam) outputs
• Build and maintain real-time anomaly detection across acquisition channels
• Support brand test design and translate NPS/sentiment signals into growth insights

Requirements

• 4+ years in data science or analytics, with at least 2 years in marketing or growth contexts
• Expert SQL and strong Python; deep familiarity with experimentation design and causal inference methods
• Experience with paid media channels and platform-level attribution mechanics
• Comfort with MMM outputs and reconciling econometric findings with channel data
• Strong communicator — able to translate statistical results into clear recommendations for non-technical stakeholders
• Bachelor\'s or advanced degree in a quantitative discipline

Preferred

• Hands-on experience with incrementality frameworks (geo-based, time-based, ghost ads)
• Familiarity with Northbeam, Rockerbox, or similar attribution tooling
• Background in fintech, consumer lending, or subscription growth