Data Scientist

MZ

(Palo Alto, California)
Full Time
Job Posting Details
About MZ
MZ, previously known as Machine Zone, is a privately held technology company based in Palo Alto, California, founded in 2008. Its flagship product, RT platform is a real-time messaging and analytics platform for managing real time data streams.
Summary
As a member of the data science team, you will develop and investigate hypotheses, structure experiments and build mathematical models to identify game optimization points that will encourage users to play our games more often, over longer periods of time, resulting in higher lifetime values. We are looking for a sharp, disciplined, and highly quantitative individual who has a passion for mobile gaming, data mining, and mathematical modeling. We are building a unique technology platform dedicated to the operation and real time optimization of free to play games.
Responsibilities
* Build mathematical models and automated machine learning tools to analyze and optimize all aspects of MZ Game Studio games, providing actionable insights in real-time * Design and implement experiments to test various hypothesis * Work in a collaborative team environment with other highly skilled specialists in gaming, statistics, machine learning, and engineering * Influence development teams to implement tools, features and/or designs - success is measured in % monetizing, retention, time spent in the game, and lifetime value
Ideal Candidate
* Bachelor's or higher in Applied Math, Statistics, Computer Science, or related field. Graduate degrees preferred * 2+ years of experience in quantitative analysis, specific experience with social games or mobile apps strongly preferred * Strong analytical and quantitative problem solving ability * Solid understanding of statistical modeling / predictive analysis/ machine learning / data mining / recommender system concepts * Familiar with R, Python or equivalent for data modeling and analysis * Familiar with SQL * Good presentation and communication skills * Ability to prioritize and track multiple projects in parallel

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