Growth Algorithm Intern (Binance Accelerator Program)

Responsibilities:

• 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.

Requirements:

• 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.

Nice-to-Haves

• 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.