Responsible for managing MLBB's traffic, optimizing user recall through push notifications, and enhancing user experience and CTR via funnel analysis. Collaborate with the algorithm team to refine recommendation algorithms, develop ARPU growth strategies, and balance user experience with revenue. Conduct ROI analysis for resource allocation, ensuring traffic goals are met without impacting main site data. Explore and implement algorithm-driven revenue opportunities to empower core business metrics.

Must Have

• Proficient in SQL for data analysis, with strong data analysis thinking.
• Master attribution analysis, causal inference, and understand ABTest methods.
• High self-learning, stress resistance, logical thinking, data analysis, and cross-departmental communication skills.

Good to Have

• Understanding of differences between various market models or relevant project experience.
• Previous experience as a data product manager or data analyst, or collaboration with algorithm teams.

Job Description

1. Responsible for the traffic of various resource positions within MLBB and using push to recall users, improving user experience and CTR through user path and funnel behavior analysis.

2. Collaborate with the algorithm team to optimize traffic scenario recommendation algorithms, formulate strategies to stimulate ARPU growth for users with different payment capabilities, balancing user experience and revenue goals.

3. ROI analysis and optimization of resource allocation. Follow up on track ecosystem traffic diversion, and while ensuring the achievement of traffic diversion goals, coordinate various resources (internal promotion, SDK, web pages, middle platform, etc.) without affecting main site data.

4. Explore more algorithm revenue opportunities, and be able to independently write algorithm solutions, implement experiments to achieve revenue growth, and empower core business indicators.

Job Requirements

1. Must-haves: Proficient in using SQL for data analysis, possessing data analysis thinking, mastering advanced methods such as attribution analysis and causal inference, and understanding strategy evaluation and ABTest methods.

2. High self-learning & stress resistance, logical thinking, data analysis, and cross-departmental communication and collaboration skills.

3. Bonus points: Have a certain understanding of the differences between various models on the market, or have actual project experience and achieved certain results.

4. Previous experience as a data product manager or data analyst, and experience collaborating with algorithm teams is also considered.