Binance Accelerator Program - Data Scientist (Recommendation/Square Community)

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means.

Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program HERE.

Current university students and recent graduates.

We are looking for a Data Scientist early careers talent to join the Binance Square team and support the development of next-generation recommendation and community intelligence systems.

Binance Square is evolving from a content feed into an intelligent Web3 financial community, helping users discover market trends, trading ideas, hot topics, and high-quality creators. In this role, you will work closely with data scientists, algorithm engineers, product managers, and business teams to improve content distribution, user engagement, and community growth through data-driven recommendation strategies.

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.

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.