Quant trader

Job Description<br>About the Role<br>We are looking for a mid‑senior level Quant Trader / Quantitative Engineer with ~5 years of hands‑on experience in systematic trading, market making, or high-frequency strategies. In this position, you will be responsible not only for research and strategy development, but also for deploying, scaling, and maintaining robust production trading systems. You will work closely with engineering, operations, and risk teams to iterate strategies end to end.<br>Responsibilities<br>Lead the full lifecycle of trading strategies: idea generation → backtesting → simulation → implementation → live monitoring and iteration<br>Develop and optimize execution algorithms (smart order routing, slicing, hedging, spread strategies)<br>Maintain, monitor, and enhance low-latency execution systems and infrastructure<br>Profile, debug, and optimize code paths to reduce latency, transaction cost, slippage<br>Build and maintain data pipelines: ingestion, cleaning, feature engineering, signal generation<br>Manage live risk: monitor exposures, margin, drawdowns, anomalies, and respond to alerts<br>Design and implement automated safeguards (circuit breakers, kill switches, watchdogs)<br>Conduct quantitative research: time-series modeling, factor research, statistical arbitrage, predictive modeling<br>Work cross-functionally: integrate your research outputs with engineers and operations; ensure robust deployment and stability<br>Analyze post-trade analytics: slippage, alpha attribution, P&L breakdowns<br>Mentor junior quants or engineers (if applicable)<br>Required Qualifications & Skills<br>~5 years’ experience in quantitative trading / systematic investment / market making<br>Advanced degree (Master’s or PhD) in Mathematics, Statistics, Physics, Computer Science, Engineering, or equivalent<br>Proficient in Python for research and prototyping<br>Experienced in at least one low-latency / performance language (C++, Rust, Go, Java)<br>Strong understanding of market microstructure, order book dynamics, exchange APIs, latency arbitrage<br>Deep quantitative skills: stochastic processes, time-series, statistics, signals, factor modeling, risk theory<br>Experience with backtesting frameworks, simulation systems, execution engines<br>Familiarity with databases (PostgreSQL, kdb+, etc.), messaging systems (Kafka, ZeroMQ, RabbitMQ), in-memory caches (Redis), and data stores<br>Skilled in performance profiling, debugging, and monitoring (e.g. tracing, logging, telemetry)<br>Ability to work under pressure, troubleshoot real-time issues, and respond to outages<br>Strong communicator, team player, capable of explaining quantitative ideas to non‑quant colleagues<br> <br>