At Glu Mobile, we build free-to-play mobile games. Our titles include Covet Fashion, Design Home, Deer Hunter, MLB Tap Sports Baseball, and our newest game, Disney Sorcerer's Arena, to name a few. One of our core challenges is balancing the risks and opportunities of a diverse set of titles. We are building a portfolio analyst team that will make game development and user acquisition investment strategy recommendations. 

Role Overview

As a Senior Economist, you will primarily consult with the marketing team. They will look to your guidance on measuring and forecasting the performance of user acquisition and other marketing efforts. Further, you will build models that allow the team to option marketing activity within and across game titles. Additionally, you will define and measure key performance indicators for games in development and in production. This is a highly visible role with a direct impact on the financial success of our game portfolio. 

You’ll most often:

• Consult with the marketing team on user acquisition and marketing efforts
• Educate studio leadership and executive leadership teams
• Build econometric models, linear and non-linear, of cross-sectional, time-series, and panel data 
• Recommend investment strategies for balanced growth
• Consult with game development teams on leading performance indicators 
• Measure the performance of in-game economies
• Pull and summarize data with traditional and big data tools 
• Design and analyze A/B, multivariate, and natural experiments 

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

• Passion for games and empathy for our players
• Excitement for working in a data rich environment 
• Enthusiasm for using analytical techniques to answer business problems
• Experience working in Unix-like and cloud environments
• Master’s degree in economics or similar with related experience

Bonus points for:

• Master’s degree in quantitative field such as statistics, economics, social science, or related field 
• Experience delivering analytics driven consulting 
• Writing re-usable code in dynamic programming languages (such Python) or statistical programming environments (such as R or Julia)
• Collaborating on code using Git