Data Scientist, Marketing

Scopely•Sunnyvale, CA

8h•$141,800 - $202,500

About The Position

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. We build products that inspire exercise, exploration, and social in-person interaction. We are seeking a quantitatively strong and business-oriented Data Scientist, Marketing to support our global Marketing organization. In this role, you will help develop measurement frameworks, analytical models, and scalable reporting solutions that inform user acquisition, brand investment, direct marketing (CRM), and long-term player growth. 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. 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.

Requirements

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.
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.

Nice To Haves

Experience supporting brand or upper-funnel marketing measurement.
Exposure to marketing mix modeling or other aggregate-level measurement frameworks.
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

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.
Contribute to 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.
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.