Staff Data Scientist
Palo Alto, CA
7 months ago
Build the future of mobile games with MZ!
As a global leader in mobile gaming, we’re dedicated to developing games the world can’t wait to experience. Games like Final Fantasy XV: A New Empire, Mobile Strike, and Game of War: Fire Age.
We build massive mobile games that break down linguistic and geographic barriers by uniting an unprecedented number of global players in one gaming world. Our team pushes the boundaries of innovation in a player-driven ecosystem.
As a studio, we are masters of our own destiny, untethered by the traditional publisher model. Every update and feature creates amazing experiences for millions of players!
Data driven decision-making is integral to game development, platform, marketing and operations at MZ. We’re looking for a sharp, disciplined, and highly quantitative data scientists with statistical modeling experience and a passion for games and digital marketing technologies to help drive informed decision-making. You will work with top-talent and cutting edge technology on, for example, but not limited to, performance marketing and next-generation games and have a unique opportunity to turn your insights into products used by millions of users. The potential candidate will have an extensive background in a quantitative field, will have experience working with large data sets, and will have some experience in data-driven decision making. The candidate should be results-driven, highly motivated to take up new initiatives and have a track record of using data analytics to drive the understanding, growth and success of a product. Your work will directly impact millions of end-users in the future. This is your chance to leave your legacy and be part of a highly successful and growth company!
What you'll be doing:
- Collaborate with colleagues across multiple teams (Marketing, Game and Data Science Engineering) on unique challenges at scale
- Design and formulate raw business problems into mathematical optimization challenges in terms of supervised / unsupervised learning, anomaly detection, experimental design, causal analysis, graph analysis, etc
- Lead and research new algorithms to assess the efficacy of marketing campaigns and for real-time bidding, fraud detection, life-time value prediction, retention framework setup, etc
- Build predictive models and automated machine learning pipelines to guide marketing campaign strategies with actionable insights, by analyzing high velocity streaming data
- Mentor junior team members to accelerate their technical growth, including modeling brainstorm, code review, project delivery, etc
- Vision quarterly roadmaps of potential projects and communicate with the management team
- Work in a collaborative team environment with other highly skilled specialists in gaming, ad-tech, statistics, machine learning, and engineering
Your background and who you are:
- MS or Ph.D. in Applied Mathematics, Statistics, Bioinformatics or a MS or Ph.D. in Computer Science with focus on Data Mining, Machine Learning.
- 6+ years of experience with development of statistical learning models and their validation with real large datasets (GB/TB)
- 6+ years of experience with R or Python, familiar with SQL
- 3+ years of experience with mining of big data streams by leveraging frameworks such as Spark, Hive, Pig, etc.
- 2+ years of mentoring experience of junior team members
- Extensive knowhow of the literature of one or more of the following: time series analysis, experiment design, classification, clustering, optimization, recommendation system, graph analysis, deep learning.
- An effective communicator – you shall be an ambassador of MZ marketing data science at external forums
- Prior experience with ads product development (e.g., DSP/ad-exchange/SSP) and established a track record of innovation would be a big plus
- Contributions to open source (e.g., R/python packages) would be a plus
- Published papers in top-tier peer-reviewed conferences and/or journals would be a plus
MZ is an equal opportunity employer and considers qualified applicants without regard to race, gender, sexual orientation, gender identity or expression, genetic information, national origin, age, disability, medical condition, religion, marital status or veteran status, or any other basis protected by law.