Senior/Lead Data Scientist (Data analyst)
— 5+ years of experience in data management and data analytics;
— experience in team management;
— proficiency in math, statistics, and probability theory;
— strong systemic and algorithmic thinking;
— experience with SQL and schema-design;
— experience with ETL tools;
— knowledge of A/B testing concepts and tools;
— experience with BI tools: Google Data Studio/ Microsoft PowerBI / Tableau etc.
— experience in building Data Warehouse;
— strong data modeling skills;
— strong data munging/processing skills;
— solid professional experience using Python for purposes of model development model implementation in production;
— knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
Will be a plus
— Understanding of ML/DS algorithms is a plus.
— Company helps mass adoption of esports and unlocking a market for hundreds of millions of gamers and thousands of advertisers and sponsors, and you too can have an impact on this ride;
— passionate, diverse, and supportive team members;
— open, transparent, and bureaucracy-free company culture;
— competitive compensation package.
— Own the monitoring of business metrics, product/launch performance and proactively communicate findings to help business focus on key decisions to improve products and services;
— present findings and recommendations to multiple levels of stakeholders, by creating visualizations of quantitative information;
— manage and execute the process of A/B testing, implement best practices;
— develop and automate reports, iteratively build and prototype dashboards to provide insights at scale;
— conduct fast search for data-driven insights;
— provide result interpretations;
— conduct large-scale data analysis to make business recommendations (e.g. what-if, cost-benefit, impact analysis);
— work with engineering teams to develop new data measurement to aid in understanding product’s usage;
— perform exploratory data analysis to find product improvement features that can drive additional value for users;
— perform complex analytics requests;
— manage the team of a few colleagues.