Senior Data Scientist, Marketing

Responsibilities

• Contribute to and own the Marketing Mix Modeling (MMM) and other aggregate measurement approaches to evaluate cross-channel and upper-funnel impact (e.g. brand) where deterministic attribution is limited.
• 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, etc.
• 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.

Requirements

• 4-8 years of experience in data science, marketing analytics, or a related quantitative role (gaming, mobile, or digital consumer experience preferred).
• Demonstrated proficiency in integrating AI agentic tools to accelerate end-to-end data science workflows.
• Proven experience in user acquisition measurement and attribution with exposure to Marketing Mix Modeling or other aggregate-level measurement frameworks.
• Understanding of full marketing funnel, including brand/awareness, social, UA, 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.

Preferred

• Experience supporting brand or upper-funnel marketing measurement.
• Familiarity with lifecycle marketing analytics, segmentation modeling, or propensity modeling.
• Experience building marketing data pipelines using Airflow, Composer, or similar orchestration tools.
• Experience working with global marketing teams across multiple regions.