This job listing expired on Aug 23, 2021
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At Glu Mobile, we love to build free-to-play mobile games. Our titles include Covet Fashion, Design Home, Deer Hunter, MLB Tap Sports Baseball, and Disney Sorcerer's Arena, to name a few. We are seeking a marketing analytics manager to grow our team of data analysts.

The Role

As the Manager, Marketing Analytics, you will build and strengthen a team of analysts and collaborate with studios on how to best optimize their games. You will work with your team to define business problems, develop analytical approaches, and craft recommendations to Growth Team and studio leadership. You will also connect findings from one game to opportunities across the portfolio. This is a highly visible role with direct impact across Glu’s portfolio.

You’ll most often:

  • Coach analysts on solving problems and delivering impactful recommendations
  • Identify and prioritize opportunities to improve marketing
  • Develop analytic approaches to marketing problems
  • Forecast the growth of player cohorts
  • Collaborate with marketing on improving campaign performance
  • Evaluate marketing strategies
  • Maintain and advance high standards of analytical quality

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

  • Passion for games and appreciation for our players
  • Excitement for working in a data rich environment
  • Experience delivering analytics driven consulting
  • Expertise using SQL to manipulate data in relational databases
  • Experience working with performance marketing systems and creative metrics to deliver significant insights to marketing teams (Product Marketing, Creative and UA)
  • Pride in growing the capabilities of your team
  • Experience managing and growing a group of analysts

Bonus points for:

  • Master’s Degree in statistics, economics, or other quantitative field and five or more years of experience
  • Experience delivering analytics driven consulting
  • Experience working in Unix-like and cloud environments
  • Writing queries in big data environments
  • Writing re-usable code in dynamic programming languages (such Python) or statistical programming environments (such as R or Julia)
  • Collaborating on code using Git
  • Building dashboards in Tableau, Looker, or similar