【About Us】
We are a leading quantitative trading firm and liquidity provider dedicated to delivering superior risk-adjusted returns. Our trading models have stood the test of time by combining comprehensive mathematical analysis, extensive financial market knowledge, and cutting-edge artificial intelligence technology solutions. We are pioneers in systematic decision-making, algorithmic execution, and active risk management. Our team consists of experienced professionals from top investment banks (such as Morgan Stanley/Merrill Lynch (Bank of America)/UBS/Macquarie) and graduates with outstanding academic backgrounds from institutions like London School of Economics/Oxford University/Nanyang Technological University/National University of Singapore.
【Responsibilities】
Financial Knowledge Graph Design and Construction
- Design knowledge graph structure based on three elements: nodes, edges, and attributes:
- Determine data entities as nodes (e.g., stocks, futures, options, companies, news events)
- Define relationship edges between entities (e.g., stock-option relationships, news-market volatility correlations)
- Specify attribute data placement (e.g., prices, volatility, sentiment scores on nodes or edges)
- Design real-time update mechanisms for dynamic changes in nodes, edges, and attributes during market fluctuations
Graph Database Implementation and Optimization
- Responsible for knowledge graph storage design and performance optimization using mainstream graph databases like Neo4j, TigerGraph, ArangoDB, or JanusGraph
- Ensure support for high-frequency queries, dynamic updates, and graph computation tasks
Integration with Large Language Models
- Combine knowledge graphs with large language models (e.g., GPT, LLM) using RAG (Retrieval-Augmented Generation) to enhance semantic understanding
- Develop intelligent Q&A and trend analysis tools to extract relevant financial insights from knowledge graphs
Real-time Data Processing and Graph Construction
- Process real-time market data, option Greeks, and news events using Apache Flink or Kafka Streams
- Implement multi-source data integration and relationship mining to ensure real-time accuracy of the knowledge graph
Trend Analysis and Strategy Support
- Mine potential market trends and risk factors based on knowledge graphs to support quantitative trading strategy generation
- Design graph computation algorithms (e.g., graph embedding, path analysis, node classification) to reveal hidden market relationships
【Requirements】
Knowledge Graph Skills
- Proficient in knowledge graph modeling (nodes, edges, attributes)
- Master key technologies in entity recognition, relationship extraction, and knowledge reasoning
- Experienced with graph databases (Neo4j, TigerGraph, ArangoDB, or JanusGraph)
- Familiar with RDF, SPARQL knowledge graph standardization (bonus)
Real-time Data Processing
- Expert in stream processing frameworks (Apache Flink, Kafka Streams)
- Experience handling financial tick data, option Greeks, and news events
Graph Computing and Machine Learning
- Familiar with common graph algorithms (PageRank, Shortest Path, GraphSAGE, Node2Vec)
- Experience with graph embeddings and neural networks (GCN, GAT)
NLP and Large Language Models
- Proficient in NLP frameworks (spaCy, Transformers)
- Familiar with LLM knowledge injection methods
Programming Skills
- Proficient in Python, Java, C++, or Scala
- Experience with distributed systems development
Industry Experience (Bonus)
- Financial industry background
- Project experience in quantitative trading or financial data analysis
Personal Skills
- Excellent system design capabilities
- Strong communication and collaboration skills
【Benefits】
- Optional remote work up to 100% – your choice; up to 25 days working abroad annually
- Competitive base salary and bonuses
- Flat organizational structure, positive team atmosphere
- Multiple company overseas trips annually
- Recreational activities including sports and board games
【Location】
- China Shanghai/Dublin, Ireland/Calgary, Canada