Binance Accelerator Program - Data Scientist (User Growth)

Participate in the design and development of algorithm strategies for user growth, including but not limited to user segmentation, intelligent recommendation, and incentive strategy optimization.

Build user lifecycle models (LTV prediction, churn warning, conversion prediction, etc.) based on user behavior data to drive precision operations and decision-making.

Design and implement A/B testing experiments to evaluate the effectiveness of growth strategies and drive continuous iteration and optimization.

Explore user characteristics and behavioral patterns using machine learning and causal inference methods to improve user retention and conversion.

Collaborate closely with product, operations, and engineering teams to translate algorithmic capabilities into measurable business growth outcomes.

Currently pursuing a Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, Artificial Intelligence, or a related field. Available to intern for at least 4 days per week, with a minimum internship duration of 3 months.

Solid foundation in machine learning, familiar with common models (e.g., LR, GBDT, DNN) and their applicable scenarios.

Proficient in Python, with hands-on experience using NumPy, Pandas, and Scikit-learn for data analysis and modeling.

Strong SQL skills, able to independently extract and analyze data from databases.

Familiarity with at least one of the following areas is preferred: recommender systems, causal inference, reinforcement learning, or operations research/optimization.

Prior internship or project experience in user growth, computational advertising, search/recommendation systems is a plus.

Strong communication skills and self-motivation, with the ability to learn quickly and solve problems independently.

Experience in data science competitions (e.g., Kaggle, Tianchi) with notable achievements.

Familiarity with growth-specific methods such as Uplift Modeling and Multi-Armed Bandit.

Experience with large-scale data processing frameworks (e.g., Spark, Flink).

Publications in machine learning, data mining, or related fields.