CFD量化风控专家（HK/MY）

职位要求 / 描述

About us Bitget is one of the world's leading digital assets ecosystems. With over 120 million registered users, Bitget has one of the most comprehensive suites of blockchain products and services available via bitget.com. Our mission is to support the growth of the digital assets industry and we believe it represents the future of finance. What we do empowers the future of finance by ensuring secure, efficient and smart digital transactions. We are one of the fastest growing companies in the digital asset sector. If you are looking for cutting-edge work, where you will have opportunities to develop your career among peers who are experts in their field, and you believe in the future of digital currency, then look no further than Bitget! What you'll do 1. User Profiling Feature Engineering & Quantitative Client Segmentation Modeling • Feature Mining: Extract and construct a quantitative feature library reflecting clients' trading habits, risk exposures, and profitability from massive, high-frequency Tick data and user behavior data (e.g., capital utilization rates, overnight position preferences, dynamic win rates, Sharpe ratios, slippage sensitivity, etc.). • Effectiveness Evaluation & Selection: Establish a rigorous feature validation framework (e.g., IV/WOE analysis, T-tests/hypothesis testing, correlation analysis, SHAP values for feature importance, etc.) to eliminate noisy factors. • Anti-Overfitting & Data Leakage Prevention: Ensure the decay rate of features in out-of-sample and live trading environments remains controllable; strictly prevent look-ahead bias (future functions) to guarantee model robustness and generalizability. • Client Segmentation & Flow Routing: Build machine learning or statistical models to accurately identify "toxic flow" versus standard retail flow. Provide quantitative decision support for the A/B Book hedging and routing engine to maximize revenue and minimize risk. 2. Risk Control Algorithm Design & Hedging Strategies • Dynamic Margin & Liquidation Algorithms: Optimize the platform's dynamic leverage rules and tiered maintenance margin models to effectively mitigate negative equity/runaway loss risks under extreme market conditions. • Risk Exposure & VaR Models: Establish a real-time risk exposure monitoring system for multi-asset classes (Forex, Commodities, Indices, and other CFDs); develop quantitative models for VaR (Value at Risk) and stress testing. • Anomalous Trading & Fraud Detection: Design anti-abuse/anti-cheating algorithms to identify and block illicit or disruptive trading behaviors in real time, such as latency arbitrage, high-frequency scalping, and market manipulation. • Automated Hedging Strategy Development: Assist in designing internal risk hedging models for B-Book and automated external routing (Straight-Through Processing/STP) algorithms for C-Book (hybrid models). 3. Engineering Implementation & System Iteration • Collaborate closely with data engineering and backend development teams to translate quantitative models into production-ready, executable code within low-latency environments. • Continuously track the on-live performance of models (analyzing backtest vs. production variance) to iterate and fine-tune algorithms. What you'll need 1. Educational Background • Bachelor’s degree or above from a top-tier university (985/211 or equivalent global ranking); major in Mathematics, Statistics, Financial Engineering, Computer Science, Physics, or related quantitative/STEM disciplines. 2. Professional Experience • 1–3 years of experience in quantitative risk control, algorithmic trading, or data science within the financial industry; backgrounds in Forex or CFD derivatives trading platforms are highly preferred. 3. Core Skills • Technical Proficiency: Expert-level command of Python (including Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, etc.) and SQL. • Analytical Foundation: Solid expertise in machine learning, statistical analysis, and financial

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