Binance Accelerator Program - Data Scientist, Risk (Machine Learning and Algorithms)
at Binance
Posted 7 hours ago
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Binance's Accelerator Program offers an immersive internship for early-career talent as a Data Scientist in Risk, focusing on machine learning and algorithms. You will work with petabyte-scale datasets on a cloud-native platform to build impactful data products that touch millions of users globally, collaborating with engineers, analysts, product managers, marketers, and business teams to develop end-to-end features, models, and AI-driven solutions in crypto. Responsibilities include Universe Risk Management through data analysis across KYC, payments, credit, and exchange operations; Personalized Services and Anomaly Detection; Blockchain-Based Data Insights; and Customer Feedback & Satisfaction Analysis to guide product improvements.
Responsibilities
- Universe Risk Management: Conduct data analysis and modeling across key domains such as KYC, payments, credit, and exchange operations. Analyze user behavior and patterns to identify risk indicators and inform decision-making
- Personalized Services & Anomaly Detection: Leverage our petabyte-scale data warehouse to perform in-depth user analysis. Build personalized services and develop automated systems for detecting abnormal user activity
- Blockchain-Based Data Insights: Extract and analyze blockchain data to generate predictive insights and tailored recommendations
- Customer Feedback & Satisfaction Analysis: Apply machine learning techniques to evaluate customer feedback and satisfaction. Provide data-backed insights to guide product and service improvements
Requirements
- Able to commit to a duration of 6 to 12 months.
- Currently enrolled as a full-time undergraduate or graduate student.
- A Master’s degree or higher in a relevant field (e.g., Computer Science, Statistics, Applied Mathematics) is preferred.
- Demonstrated experience in developing machine learning models at scale, from experimentation through to deployment.
- Solid understanding of modern machine learning techniques and their underlying mathematics, including classification, recommendation systems, and optimization methods.
- Practical experience handling large-scale datasets; familiarity with distributed data processing is an advantage.
- Proficiency in Python, Java, or Scala is preferred.
- Experience with deep learning frameworks such as TensorFlow or PyTorch is a plus.
- Hands-on experience in deploying machine learning models in production is a strong asset.

