Senior Quantitative Trader (Prediction Market)

KuCoin Exchange; Hong Kong; On-site; HKD 783,699 - 1,175,548; Full time

Benefits offered by this job

Performance-linked bonuses

Access to proprietary datasets

Autonomy in research direction

Job summary

KuCoin Exchange in Hong Kong is seeking a talented individual to manage a profitable trading book with responsibilities encompassing PnL, risk, and capital allocation. Your expertise in machine learning and deep learning will be leveraged to design predictive trading signals and optimize systematic strategies across various market conditions.

The ideal candidate will have a strong background in quantitative fields, hands-on experience with AI tools, and a proven track record of developing successful trading strategies. Join our high-caliber team and influence our trading decisions significantly.

Qualifications

• Proven experience managing profitable systematic strategies.
• Deep expertise in applying machine learning to financial time series.
• Hands-on experience with AI agents and tools.

Responsibilities

• Manage a profitable trading book with full responsibility for PnL.
• Design predictive signals using deep learning and machine learning.
• Mentor junior researchers and improve research methodologies.

Skills

Machine learning

Deep learning

Python

Statistical analysis

Risk management

Education

Bachelor's, Master's, or PhD in a quantitative field

Tools

PyTorch

TensorFlow

Scikit-learn

pandas

NumPy

Job description

• Own and manage a profitable trading book, taking full responsibility for PnL, risk, and capital allocation across delta-one instruments (spot, perpetuals, futures) spanning multiple horizons—from high-frequency liquidity strategies (CEX/cross-exchange arbitrage, market making, order book micro-alpha) to mid-frequency statistical arbitrage, CTA/trend, and cross-sectional alpha across the crypto universe.
• Lead the design and deployment of predictive signals using deep learning and machine learning (sequence models, transformers, gradient boosting, supervised/unsupervised methods) applied to tick, order book, mid-frequency, and on‑chain data.
• Build and trade cross-sectional and time‑series strategies—relative‑value/stat arb baskets, factor models, momentum/mean‑reversion, and portfolio construction with sound risk and capital allocation.
• Leverage AI agents and LLM‑based tooling to accelerate the research process—rapid prototyping, automated feature/hypothesis exploration, code generation, and scaling research throughput without sacrificing rigor.
• Drive the full research lifecycle end to end: hypothesis, feature engineering, model development, rigorous backtesting, walk‑forward/out‑of‑sample validation, and live deployment with continuous performance monitoring.
• Account for the microstructure and execution realities of each horizon—latency, slippage, fill probability, fee tiers, capacity, turnover, and transaction‑cost‑aware portfolio construction.
• Mentor junior researchers, set research direction, and raise the bar on methodology, reproducibility, and signal quality across the team.
• Partner closely with engineering to harden signals into production systems and improve the research‑to‑production pipeline.
• Stay at the frontier of crypto market microstructure, DeFi, and emerging data sources, translating fresh ideas into tradable edge.

What We Look For In You

• Bachelor's, Master's, or PhD in a quantitative field: Mathematics, Physics, Computer Science, Statistics, Engineering, or related (top‑tier university strongly preferred).
• Proven track record of researching, deploying, and managing profitable systematic strategies across one or more of: HFT/liquidity provision, mid‑frequency statistical arbitrage, CTA/trend, or cross‑sectional alpha—in crypto or traditional markets.
• Deep expertise applying machine learning and deep learning to financial time series, with hands‑on experience in PyTorch/TensorFlow/JAX and the full scikit‑learn/pandas/numpy stack.
• Hands‑on experience using AI agents and LLM‑based tools to drive research—from prototyping and code generation to automating data exploration and accelerating the research loop.
• Strong command of the statistical pitfalls of systematic research (overfitting, look‑ahead bias, multiple testing, regime shifts) and of horizon‑appropriate concerns—order book modeling and tick data for HFT; factor construction, cross‑sectional ranking, and portfolio optimization for MFT/cross‑sectional work.
• Production‑grade engineering instincts—clean, reproducible, performant code—with the judgment to balance rapid prototyping against robustness.
• Ownership mentality: comfortable being accountable for a live book and making real‑time risk/capital decisions.
• Strong communication skills and the ability to lead and influence within a research team.

Nice-to-Haves

• Experience with crypto CEX APIs, low‑latency infrastructure, and tick data processing.
• Rust/C++ for performance‑critical components.
• Familiarity with reinforcement learning for execution, market making, or allocation.
• Portfolio construction, risk modeling, and transaction‑cost analysis at scale.
• On‑chain/DeFi research experience.

What We Offer

• Full ownership of a meaningful book with direct PnL impact and a highly competitive package: strong base, performance‑linked bonuses, and crypto upside.
• Access to rich proprietary datasets, premium market data feeds, and high‑performance research and execution infrastructure.
• A lean, high‑caliber team where your signals go live fast and your decisions matter.
• Autonomy to set research direction and shape the strategy roadmap.