Data Science Lead

Epic Games
BLANK,BLANK,Multiple LocationsPosted 11 April 2026

Job Description

WHAT MAKES US EPIC? At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating. Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development. ANALYTICS What We Do Our Data Analytics teams build powerful stories and visuals that inform the games we make, the technology we develop, and business decisions that drive Epic. What You'll Do Epic Games is looking for a hands-on Data Science Lead to join the Gameplay Analytics team. You’ll lead a team of Data Scientists and Data Programmers and apply mixed methods to own the end-to-end life cycle of creating data assets, reports, insights, to impact the direction of efforts across Epic’s portfolio of products and ecosystems, with a particular focus on our Shooters Games (Battle Royale, Reload, OG, Blitz). The successful candidate will have the ability to apply a range of data technologies, statistical methods, and visualization methods to influence development and product teams, to manage a small team and to partner with stakeholders across all parts of the business. In this role, you will Partner with design, game development, and product management counterparts to identify opportunities for our first party Games and the business, and use data to develop metrics and insights that shape the execution of the strategy Work directly with programming and data platform teams to properly scope and capture the data required to unlock insights, data models Transform raw data into data models, production metrics, reports, and insights Continuously evaluate the effectiveness of metrics and align them with long-term ecosystem growth Work with design, product, and analytics teams to quickly and rigorously design, test, and learn from experiments to improve our games Lead a small team of data analysts, help mentor them, and set the direction of our analytics approach What we're looking for At least 5 years of industry or relevant experience, with proven interest in and knowledge of live service video games At least 2 years of experience managing a team of data professionals Strong product intuition and demonstrated background in influencing products by applying data and measurement to drive alignment Experience with working with development teams to instrument data and ensure continued data quality Experience with writing efficient and scalable SQL over billions of rows; it is a plus if you are familiar with distributed computing using Spark (Scala or PySpark) Development experience in Python analytics ecosystem Familiarity with supporting operational processes including: Code version control (Github or similar), Orchestration (Airflow), AI coding tools (Claude Code) Understanding of experimental design and A/B testing techniques Advanced statistical training and a thorough understanding of the associated strengths and weaknesses of different statistical approaches is a plus Experience in a “full-stack” environment - data file ingest, ETL, data storage interaction, application development, communication of results to storage systems, code version control is a plus This role is open to multiple locations in North America (including NYC). Pay Transparency Information The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participat ... (truncated, view full listing at source)
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