Introduction

At Glu Sports, we’ve been building successful mobile sports and outdoor games for nearly a decade. Our recent titles include MLB Tap Sports Baseball 2020 and WWE: Universe, with development of the next generation Deer Hunter and other games underway. We’re scrappy, iterative, and collaborative. And we need your help to uncover robust insights into our games and deliver better experiences for our players. 

Role Overview 

As an Associate Game Data Analyst, you will be the analytical partner for game teams at the studio. You will work closely with Game Designers, Product Managers, and Game Engineers to transform subjective discussions about product and player behavior into concise analytic questions that deliver actionable answers. You will work together with and mentor other Game Analysts and Data Scientists at the studio and across the company, communicating findings to executives and impacting development of other titles in the Glu portfolio. This is a high visibility role that will have a direct impact on the success of some of Glu’s biggest titles. 

You'll most often:

• Work with game teams to measure and optimize performance of titles in beta and after worldwide launch 
• Use big data tools such as Hive, Presto and Redshift to structure billions of rows of data to answer challenging business and design questions 
• Consolidate findings and make connections across multiple analyses and KPIs, communicating the health of the business and contributing to strategic planning 
• Design and interpret A/B and multivariate experiments 
• Develop data products that allow game teams to self-serve custom insights 
• Define telemetry and success metrics for game features and gameplay mechanics, validating game designs and providing feedback on player behavior 
• Continuously learn new technologies that advance analytic functions at Glu 

You'll be a great fit for this role if you have:

• Passion for games and empathy for our players 
• High proficiency with SQL at “big data” scales, especially in distributed environments 
• Comfort in UNIX-like platforms 
• Experience in applied statistical inference with statistical modeling platforms such as R, Stata, SPSS, or Mathematica 
• Experience with modern dynamic programming languages (such as Python, Julia, Wolfram Language, or similar) 
• Bachelor’s degree in Computer Science, Economics, Mathematics, Statistics, Business Administration, Marketing or related field 

Bonus points for:

• A graduate degree in a related technical field or social science 
• Predictive modeling or machine learning experience 
• Data modeling, architecture, or engineering experience