Building the underlying platform and ledger for digital ownership.
We are looking for an entry-level Quantitative Mobile Analyst to join our team and help us use data, insights, and performance-driven tactics to grow our consumer app business.
As a Quantitative Mobile Analyst, here are some examples of what you may work on:
Perform deep analysis of product and user quality (analysis, revenue distribution, LTV modeling, etc.) and source quality and efficiency (pricing, referral source evaluation, click-through analysis, performance management, etc.).
Use statistical and data visualization techniques to analyze patterns related to economics, user behaviors, and other key performance metrics.
Run data-driven experiments and campaigns across multiple sources of user acquisition (Facebook, Google, etc.).
Develop and implement data collection and analysis systems and processes.
Work with development team on new tools for data management and visualization.
Identify patterns and trends over large and complex datasets.
Work in cross-functional teams to meet specific goals.
Little to no experience in mobile user acquisition or free-to-play gaming, but have a strong passion for data!
Healthy appreciation for mobile games.
0 to 2 years data analysis experience.
Very strong math skills, especially statistics.
Excellent with MS Excel, including VBA, data analysis tools, pivot tables, and charting.
Strong knowledge and experience with SQL, time series, and other types of databases.
Strong analytical skills with an ability to collect, organize, and disseminate findings with detail and accuracy.
Good communication, organization, and problem solving skills.
Detail oriented with outstanding aptitude for self-growth.
Some level of experience in an office setting (internship with investment bank, consulting firm, tech company, etc.)
BS degree in quantitative field (math, statistics, finance, economics, computer science, physics, or related field).
Nice to have:
Knowledge in the areas of probability, linear algebra, and calculus.
Experience with statistical packages such as R or SAS.