Jiawei Tang

• Monitored real-time and intraday trading risk across derivatives accounts, tracking PnL volatility, exposure changes, margin utilization, and abnormal trading behavior during high-volatility market conditions.

• Designed and enforced pre-trade and intraday risk limits across traders and trading entities, including position, exposure, and loss thresholds, with structured escalation and audit trails.

• Conducted intraday stress assessments during market surges, identifying concentration and liquidity risks beyond standard margin metrics.

• Investigated risk breaches, rejected orders, and execution anomalies, working closely with trading, technology, and operations teams to diagnose root causes and prevent recurrence.

• Built and maintained automated risk monitoring and reporting pipelines in Python, achieving ~90% automation of intraday and daily risk surveillance and materially reducing response latency.

• Produced daily and intraday risk reports for senior management, synthesizing exposure, drawdowns, and emerging risk signals into actionable insights.

• Gained hands-on familiarity with exchange-level risk controls, including messaging violations, self-trade prevention, and platform-level throttles.

• Supported end‑to‑end client onboarding by
  performing CIP and CDD checks in accordance
  with BSA/AML and USA PATRIOT Act
  requirements. Verified customer identities,
  reviewed beneficial ownership information for
  legal entities, and completed OFAC, PEP, and
  adverse media screenings to ensure
  compliance with regulatory standards.
• Reviewed and analyzed daily
  transaction‑monitoring alerts for unusual or
  high‑risk payment activity. Assessed
  transactions against customer profiles,
  identified potential red flags, documented
  findings in case management systems, and
  escalated suspicious cases for further
  investigation when necessary.

• Conducted quantitative research on ETF relative-value strategies, achieving ~65% hit ratio and ~70 bps average return per trade in backtests.

• Designed and implemented a Python-based backtesting and analysis framework, including performance attribution and scenario analysis.

• Built interactive post-trade analytics dashboards (Bokeh) to support portfolio-level decision making.

• Processed and analyzed large historical datasets using optimized data structures, multithreading, and efficient indexing techniques.