Data Scientist, Marketing

- Build and maintain robust ETL pipelines that ingest, transform, and validate UA data from ad networks, MMPs, and internal systems. - Develop and refine predictive LTV (pLTV) models to enable faster optimization of UA campaigns based on early user signals. - Explore, develop, and refine AI-based systems that are able to answer common data inquiries from stakeholders, as well as quickly diagnose data pipeline issues. - Contribute to marketing mix modeling (MMM) and other aggregate measurement approaches to evaluate cross-channel and upper-funnel impact. - Support testing frameworks (e.g., geo experiments, holdouts, incrementality tests) to evaluate campaign effectiveness in privacy-constrained environments. - Partner with Finance and UA teams to align on forecasting methodologies and investment strategies driven by pLTV and payback periods. - Build and maintain reliable datasets and ETL workflows that ingest and transform marketing data from ad platforms, CRM systems, social channels, and internal data sources. - Support measurement and optimization of direct marketing channels including email, push notifications, in-app messaging, and other CRM/lifecycle campaigns. - Partner with Marketing stakeholders to provide actionable insights on targeting, segmentation, messaging effectiveness, and channel strategy. - Contribute to scalable dashboards and standardized reporting that enable self-serve marketing analytics. - Ensure data quality, documentation, and consistency across marketing data pipelines. - Leverage modern AI/ML tools (e.g., automated modeling workflows, AI coding assistants) to improve analysis speed, code quality, and documentation. - Identify opportunities to automate recurring reporting and insight generation for marketing stakeholders. - Contribute to responsible and thoughtful adoption of AI-powered analytics tools. - 1–5 years of experience in data science, marketing analytics, or a related quantitative role (gaming, mobile, or digital consumer experience preferred). - Understanding of marketing measurement across paid media, brand/awareness, social, and direct marketing channels (e.g., email, push, CRM). - Familiarity with core performance metrics such as CAC, ROAS, retention, engagement, and LTV. - Experience working with marketing data from ad platforms, CRM systems, or aggregate reporting environments. - Proficiency in SQL and experience using Python (or similar) for analysis. - Experience working with large datasets in a cloud data warehouse (e.g., BigQuery) and building dashboards in BI tools (e.g., Looker). - Strong analytical, communication, and stakeholder management skills in a cross-functional environment.