Scopely

Scopely

Data Scientist, Marketing

US - San Francisco, United States; US - Sunnyvale, United States

Sponsorship not specified$142k-$203kDetected 42 days ago
PythonSQLBigQueryMachine LearningAirflowData EngineeringData ScienceData VisualizationStakeholder ManagementMarketing AnalyticsCRMForecastingCommunication

About the role

  • This role is ideal for someone who combines strong technical skills with intellectual curiosity and a desire to understand how marketing investments translate into long-term player value.
  • For candidates in CA, CO, NJ, NY, and WA, the annual salary range is provided below.

Responsibilities

  • 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.

Requirements

  • 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 supporting brand or upper-funnel marketing measurement.
  • Familiarity with lifecycle marketing analytics, segmentation modeling, or propensity modeling.
  • Experience working with global marketing teams across multiple regions.
  • Understanding of marketing measurement across paid media, brand/awareness, social, and direct marketing channels (e.g., email, push, CRM).
  • 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.
  • Plus If…
  • Exposure to marketing mix modeling or other aggregate-level measurement frameworks.
  • Experience building marketing data pipelines using Airflow, Composer, or similar orchestration tools.
  • For candidates in CA, CO, NJ, NY, and WA, the annual salary range is provided below. In addition to base pay, employees may be eligible for equity, bonuses, and a comprehensive benefits package, including healthcare benefits, retirement benefits, pet insurance, paid holidays, paid Scopely free days, and unlimited paid time off. Base pay offered may vary depending on job-related knowledge, skills, and experience.

Nice to have

  • 1-5 years of experience in data science, marketing analytics, or a related quantitative role (gaming, mobile, or digital consumer experience preferred).

Skills

  • Scopely is a global gaming company whose mission is to inspire play every day.
  • Niantic (a division of Scopely)'s mission is to inspire people to explore the world, together.
  • Demonstrated effective use of AI and a forward-thinking mindset into how AI will change day-to-day work in data and marketing is mandatory.

Compensation

  • For candidates in CA, CO, NJ, NY, and WA, the annual salary range is provided below.
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This listing is sourced directly from Scopely's careers page and normalized into a canonical job model.