Responsibilities

Analyze user behavior, content quality, creator performance, and feed consumption patterns to identify opportunities for recommendation improvement.
Support the optimization of key recommendation scenarios, including Feed, Hot Tab, Topic, News, Trading Analysis, and creator distribution.
Build and refine data metrics for content quality, user interest profiling, creator quality, community health, and trading-related content.
Participate in recommendation strategy design, including recall, ranking, cold-start, diversity, personalization, and traffic allocation.
Conduct A/B testing, metric monitoring, bad-case analysis, and experiment deep dives to evaluate algorithm and product impact.
Explore the application of LLMs and multimodal models in content understanding, topic tagging, hot event detection, and personalized distribution.
Work with engineering teams to improve data pipelines, feature logging, experiment tracking, and recommendation system efficiency.

Requirements

Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Strong analytical skills and solid understanding of statistics, machine learning, and data mining.
Proficient in SQL and at least one programming language such as Python.
Familiar with recommendation systems, ranking models, user profiling, A/B testing, or causal analysis.
Good business sense and ability to translate data insights into product or algorithm improvements.
Strong communication skills and ability to work cross-functionally with product, algorithm, engineering, and operations teams.
Interest in Web3, crypto, financial markets, online communities, or content recommendation is a plus.

Originally posted on Himalayas