Trading Risk Control Strategy Expert

Gate; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Gate is seeking an experienced risk-control engineer to design and operate risk models for core trading products, including pre-warning, real-time interception, and post-event analysis. You will work with high-frequency data to detect price manipulation, wash trading, fake orders, and cross-market arbitrage.

The role involves feature engineering on order book data, transaction logs, and fund transfers to build robust risk profiles, along with margin risk model construction and threshold

Qualifications

• Bachelor's degree or above in Computer Science, Statistics, Mathematics, Financial Engineering, or related fields.
• 3+ years of experience in risk control model/strategy development, preferably in exchanges or brokerages.
• Proficiency in Python, Java, or Go with strong data processing and modeling skills.
• Familiarity with machine learning models like XGBoost, LightGBM, Logistic Regression, and time-series anomaly detection.
• Experience with graph databases (Neo4j, NebulaGraph) and community detection algorithms (Louvain, LPA).
• Familiarity with real-time stream processing technologies such as Flink and Kafka for millisecond-level risk interception.

Responsibilities

• Build risk control models and strategy systems for core trading products, covering pre-warning, real-time interception, and post-event analysis.
• Design and iterate risk strategies to identify price manipulation, wash trading, fake orders, and cross-market arbitrage abuse using high-frequency data.
• Perform feature engineering and variable mining based on order book data, transaction logs, user positions, and fund transfers to build risk profiles.
• Participate in margin risk model construction, including liquidation model optimization and bankruptcy warning threshold optimization.
• Use graph computing and community detection algorithms to identify coordinated trading groups and related account wash trading.
• Establish a monitoring system for trading risk indicators to support business and compliance decisions.
• Explore AI/LLM applications in trading risk control, such as intelligent anomaly detection and automated risk attribution.

Skills

Python

Java

Go

Data processing

Modeling

Education

Bachelor's degree in CS/Math/Finance

Tools

Neo4j

NebulaGraph

Flink

Kafka

XGBoost

LightGBM

Job description

Responsibilities

• Build risk control models and strategy systems for core trading products, covering pre-warning, real-time interception, and post-event analysis
• Design and iterate risk strategies to identify price manipulation, wash trading, fake orders, and cross-market arbitrage abuse using high-frequency data
• Perform feature engineering and variable mining based on order book data, transaction logs, user positions, and fund transfers to build risk profiles
• Participate in margin risk model construction, including liquidation model optimization and bankruptcy warning threshold optimization
• Use graph computing and community detection algorithms to identify coordinated trading groups and related account wash trading
• Establish a monitoring system for trading risk indicators to support business and compliance decisions
• Explore AI/LLM applications in trading risk control, such as intelligent anomaly detection and automated risk attribution

Requirements

• Bachelor's degree or above in Computer Science, Statistics, Mathematics, Financial Engineering, or related fields
• 3+ years of experience in risk control model/strategy development, preferably in exchanges or brokerages
• Proficiency in Python, Java, or Go with strong data processing and modeling skills
• Familiarity with machine learning models like XGBoost, LightGBM, Logistic Regression, and time-series anomaly detection
• Experience with graph databases (Neo4j, NebulaGraph) and community detection algorithms (Louvain, LPA)
• Familiarity with real-time stream processing technologies such as Flink and Kafka for millisecond-level risk interception

Preferred Qualifications

• Familiarity with TradFi financial rules (Stocks, ETF, Forex) and ability to migrate traditional compliance models to TradFi scenarios
• Practical experience applying LLMs to risk control scenarios, such as user behavior recognition or automated risk reporting