Senior Data Scientist, Marketing

Scopely•San Francisco, CA

1d•$17,100 - $253,000

About The Position

Scopely is seeking a quantitatively adept and business-minded Senior Data Scientist, Marketing, to support its global marketing organization. This role operates at the intersection of data science, deep marketing domain expertise, and AI proficiency. The successful candidate will help develop measurement frameworks, analytical models (e.g., Marketing Mix Modeling), and scalable reporting solutions that inform user acquisition, brand investment, CRM efforts, and long-term player growth. The role also involves utilizing AI-augmented workflows to accelerate the end-to-end data science lifecycle. This position is ideal for an individual who combines strong technical skills with intellectual curiosity, a forward-thinking perspective on AI's transformative potential in data science, and a desire to understand how marketing investments translate into long-term player value.

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.

Nice To Haves

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.

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.
Identify opportunities to automate recurring reporting and insight generation for marketing stakeholders.
Leverage modern AI/ML tools (e.g., automated modeling workflows, AI coding assistants) to improve analysis speed, code quality, and documentation and bridge the gap between technical AI-driven insights and business stakeholders to ensure output is accurate and aligned with complex business logic.
Ensure data quality, documentation, and consistency across marketing data pipelines.
Contribute to responsible and thoughtful adoption of AI-powered analytics tools to ensure maintainability and long-term knowledge sharing.