Data Scientist

About the Role: We are seeking a Senior Data Scientist to lead Marketing Mix Modeling (MMM) and conversion/attribution analytics end-to-end. You will build models, ship pipelines in collaboration with Data Engineers, and work directly with Sales, Strategy & Insights, and Planning teams to turn measurement into revenue. This is not a research-only role — we need someone who has shipped MMM and statistical models into production systems, influenced real ad spend decisions, and can operate fluently with both Data Science and Ad Sales leadership. What You Will Do: MMM & Media Planning (~60%) Own MMM end-to-end: data preprocessing, transformation, ingestion, model development, validation, calibration, and production deployment Collaborate with Data Engineers to build and maintain MMM pipelines on GCP that ingest spend, exposure, and outcome data across linear TV, audio, and digital channels Translate model outputs into actionable planning recommendations: budget allocation, channel mix, flighting strategy, saturation curves Partner with Ad Sales tech teams to integrate MMM outputs into internal web tools used for audience insights, planning, and campaign measurement Calibrate MMM outputs against incrementality tests and lift studies; reconcile MMM with MTA and platform-reported metrics Conversion & Attribution Analytics (~30%) Build and productionize multi-touch attribution (MTA) models integrated with identity graph and clean room infrastructure Work with 1P, 2P, and 3P conversion data (pixels, TV exposure data, digital/streaming viewership, retail/sales, search activity, etc.) to measure campaign outcomes Execution & Stakeholder Ownership (~10%) Ship production code and own end-to-end model systems in collaboration with engineering teams (Data Engineers, Full-stack Developers, Architects) Present and defend measurement work to senior leadership (VP+) and external stakeholders (agencies, advertisers), translating technical outputs into business recommendations Operate independently, turning business initiatives into production-ready measurement products Must-haves: 7+ years of applied data science experience, with at least 4 years focused on MMM and marketing/media measurement Proven production experience: building and shipping data pipelines and model-serving systems (not just notebooks) Strong Python/R and SQL; experience with modern data stack (cloud warehouses, orchestration, CI/CD) Experience with incrementality testing, lift studies, and MTA, including experimental design and reconciling conflicting measurement signals Experience with MMM frameworks (e.g., Meridian, PyMC, Robyn, LightweightMMM) and understanding tradeoffs vs bespoke models Strong stakeholder communication skills, including presenting to VP-level leadership and external partners Strongly preferred: Experience at a publisher, broadcaster, streaming platform, ad-tech company, or measurement/agency environment Familiarity with ACR data, set-top box data, or TV/video viewership data Experience with third-party conversion data providers (e.g., NIQ, Polk, EDO, etc.) Exposure to both pre-sales (planning/recommendation) and post-sales (outcomes/renewal) measurement workflows GCP experience (BigQuery, Vertex AI, Composer) Nice-to-haves: Causal inference expertise (GeoLift, CausalImpact, synthetic controls, uplift modeling) Experience with clean rooms (Snowflake, AWS, Habu, InfoSum) Knowledge of identity resolution and cross-platform measurement