We are seeking an experienced Data Scientist to join our team, focusing on algorithm-driven solutions. This role will support advanced on-chain trading and market data products as well as internal business analytics, optimizing user experience, enhancing trading efficiency, and driving business decisions through data insights. You will collaborate closely with product, engineering, and business teams to develop and deploy contextualized algorithm models for key functionalities, including trading opportunity discovery, on-chain entity identification, trading asset recommendations, user behavior prediction, and user profiling.
Algorithm Development and Optimization: Design, implement, and iterate on recommendation algorithms, community detection models, user profiling systems, behavior attribution models, and behavior prediction systems. Develop customized algorithms for advanced on-chain trading scenarios (e.g., real-time signal detection, risk behavior prediction) to enhance product experiences.
Data Analysis and Insights: Analyze user behavior data, transaction logs, and internal business metrics to provide actionable insights for decision-making. For example, identify user churn causes through behavior attribution analysis or optimize product and market strategies using predictive models.
Contextualized Applications: Apply machine learning, deep learning, and statistical analysis to uncover user behavior and business patterns, deploying algorithms in real-world business scenarios, including but not limited to:
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Recommendation Systems: Personalized trading product recommendations to improve user conversion rates.
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User Profiling: Build multi-dimensional user tagging systems to support precision marketing and risk management.
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Behavior Attribution: Quantify the impact of user behavior on business outcomes, such as analyzing core user preferences.
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Behavior Prediction: Forecast future user actions (e.g., trading intent, churn risk) to support product iterations.
Cross-Team Collaboration: Partner with business analysts to validate model performance, work with product teams to design product features, and collaborate with engineers to deploy models into production environments.
Research and Innovation: Stay updated on the latest advancements in algorithms (e.g., deep learning in recommendation systems, causal inference in behavior attribution) and integrate them into the team to enhance capabilities.
Data Governance: Ensure data quality, privacy compliance, and optimize data pipelines to support large-scale computations.
Education: Bachelor’s degree or higher in Computer Science, Statistics, Mathematics, or a related field; Master’s or Ph.D. preferred.
Experience: Minimum of 3 years in data science or related fields, with preference for candidates with experience in financial, trading, or e-commerce platforms.
Technical Skills:
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Proficiency in programming languages such as Python or R, and data processing libraries (e.g., Pandas, NumPy, Scikit-learn).
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Deep expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) with hands-on deployment experience.
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Strong knowledge of core algorithm domains, particularly:
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Recommendation Algorithms: Collaborative Filtering, Content-Based, Deep Learning-based (e.g., Neural Collaborative Filtering).
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User Profiling: Clustering, Feature Engineering, Graph-based Profiling.
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Behavior Attribution: Multi-Touch Attribution, Causal Inference (e.g., Uplift Modeling).
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Behavior Prediction: Time-Series Forecasting, RNN/LSTM, Survival Analysis.
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Experience with big data tools (e.g., Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
Soft Skills: Strong business acumen to translate algorithms into business value; excellent communication skills to explain complex models to non-technical stakeholders; strong team collaboration spirit; fluent in English with the ability to read technical literature.
