About the Company
We are a global digital asset trading platform operating across multiple regions. Our products span spot and derivatives trading, quantitative trading, and financial infrastructure. We work as a 100% remote team, building systems where accuracy, resilience, and trust matter under real market conditions.
About the Role
We are hiring a Quant Trading Systems PM to define the financial logic, product requirements, and workflows for our trading middle- and back-office systems. The scope includes market making, hedging, clearing, PnL calculation, risk controls, reconciliation, and reporting.
This role requires strong trading knowledge and exceptional attention to detail. You will turn complex financial rules, cross-market differences, and edge cases into clear specifications that engineering and QA teams can implement and verify.
What You’ll Do
Define Trading and Financial Logic
Define consistent business rules across market making, hedging, clearing, PnL accounting, risk control, and reconciliation. Establish clear definitions for pricing currency, settlement currency, FX timing, fee attribution, positions, and exposure.
Build Risk and Reporting Capabilities
Design cross-market exposure views, risk limits, alert thresholds, PnL reports, and reconciliation frameworks. Enable teams to detect risk concentrations, reconciliation breaks, and unexplained balance discrepancies early.
Identify System Gaps and Edge Cases
Proactively examine system behavior under abnormal market conditions and cross-market setups. Identify issues such as FX timing mismatches, incorrect exposure calculations, inconsistent PnL logic, and reconciliation coverage gaps before they reach production.
Align Cross-Team Business Rules
Align API contracts, data definitions, and business rules across trading, strategy, risk, and engineering teams. Turn verbal agreements and implicit assumptions into written, testable specifications.
Drive Delivery and Incident Improvements
Break business requirements into executable tasks with clear priorities, acceptance criteria, and test cases. Participate in launch reviews and incident investigations, identify root-cause logic gaps, and drive remediation through completion.
What We’re Looking For
Trading Systems Background
Hands-on product ownership experience within an exchange, brokerage, market maker, quantitative trading firm, or trading systems team. You have directly worked on positions, PnL, risk, clearing, hedging, or reconciliation workflows.
Strong Trading Knowledge
Solid understanding of spot and derivatives products, including futures, perpetuals, and options. You are comfortable working with orders, executions, positions, average price, realized/unrealized PnL, margin, funding rates, exposure, and FX conversion.
Data Verification Skills
Experience working with trading APIs and raw execution or position data. You can use SQL or basic scripts to independently verify PnL, exposure, and reconciliation results instead of relying entirely on engineering.
Structured Product Thinking
You can identify hidden assumptions, define ambiguous business boundaries, and turn complex financial rules into implementation-ready PRDs, process flows, and acceptance criteria.
Cross-Functional Communication
You can establish clear agreements across trading, quantitative research, risk, engineering, and QA teams. Working proficiency in both Chinese and English is required.
Bonus Points
- Scripting ability in Python, Go, C++, or Rust.
- Experience with quantitative strategy management, risk, clearing, or reconciliation platforms.
- Business knowledge of market making, hedging, liquidity management, or cross-market arbitrage.
- Familiarity with US, Hong Kong, or Korean equities, or digital asset derivatives.
Real-World Challenges You Will Solve
Multi-Currency PnL Consistency
If a product is traded in local currency but settled in USD, how would you define a mathematically consistent PnL model across the relevant positions and cash flows?
Leveraged Exposure Calculation
For the hedge leg of a 2x leveraged ETF, should real-time exposure be calculated using the nominal multiplier or the product’s effective leverage?
Multi-Platform Reconciliation
How would you design reconciliation dimensions and alert thresholds across multiple platforms to identify discrepancies before they grow into financial losses?