Quant Trader

NEWBRIDGE ALLIANCE PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

A financial trading firm in Singapore is seeking a Senior Quantitative Trader to develop and execute automated trading strategies. The ideal candidate should have 5–10 years of experience in systematic alpha trading and a proven ability to generate alpha at scale. Successful applicants will work with advanced statistical techniques to design strategies and manage the trading pipeline, ensuring optimal execution under latency constraints. This role offers competitive compensation and significant autonomy in research and execution.

Benefits

Performance-aligned payout structure

True autonomy in research and execution

Qualifications

• 5–10+ years of live trading experience in systematic alpha trading.
• Demonstrated track record of persistent alpha with Sharpe > 1.5.
• Proficient in Python, C++, or Rust with distributed computing experience.

Responsibilities

• Design and deploy alpha-generating strategies using advanced statistical techniques.
• Conduct high-resolution market microstructure analysis.
• Implement execution frameworks leveraging smart order routing and custom execution algorithms.

Skills

Live trading experience

Python

C++ or Rust

KDB/Q

Statistical analysis

Execution frameworks

Education

MSc/PhD in applied mathematics, statistics, CS, or financial engineering

Job description

Position: Senior Quantitative Trader – Systematic Alpha & Execution

We are actively seeking a high-calibre Systematic Quant Trader with a strong alpha pedigree, capable of full lifecycle strategy ownership from signal research to execution implementation within a high-throughput, multi-asset environment. The ideal candidate operates at the intersection of alpha signal generation, execution microstructure, and portfolio construction , and brings a demonstrated ability to generate uncorrelated PnL at scale.

Role Overview:

The trader will be responsible for deploying research-driven, fully automated trading strategies across equities, futures, or liquid macro products. You will manage real-time signal ingestion, risk-normalised portfolio weights, and execution logic under latency constraints , with direct access to infrastructure, capital, and bespoke research tooling. You are expected to manage the entire research-to-production pipeline , including alpha mining, regime modelling, transaction cost estimation, and performance attribution.

Core Responsibilities:

• Design and deploy alpha-generating strategies across stat arb, medium-frequency, and short-horizon signals using advanced statistical and ML techniques (e.g. Bayesian optimisation, tree-based models, PCA, feature orthogonalisation).
• Conduct high-resolution tick-level market microstructure analysis , including order book dynamics, spread capture, adverse selection models, and queue position management.
• Implement execution frameworks leveraging smart order routing (SOR), schedule-based execution (VWAP/TWAP), and custom execution algos sensitive to real-time volatility and liquidity.
• Manage and monitor risk-adjusted capital allocation via volatility targeting, signal de-correlation, turnover optimisation, and capacity-aware constraints.
• Interface with quant researchers and low-latency engineers to productionise models, calibrate execution engines, and deploy code into live environments under strict performance SLAs.
• Backtest and stress test strategies using multi-threaded simulation engines across multiple data regimes (pre/post-fee, post-TCA, slippage-aware).
• Proactively identify signal decay, latency arbitrage windows, execution drag, or regime shifts through ongoing analytics and internal tooling.

Required Expertise:

• 5–10+ years of live trading experience in systematic alpha trading, ideally within a prop, HFT, or multi-manager hedge fund model.
• Demonstrated track record of persistent alpha , ideally with Sharpe > 1.5 over multiple market regimes and statistically significant out-of-sample PnL.
• Proficient in Python, C++ (or Rust), KDB/Q , with experience in distributed computing environments and event-driven architecture (e.g. Kafka, Redis, custom OMS/EMS).
• Expertise in real-time signal execution integration , from model inference to order routing under millisecond-level latencies.
• Strong grasp of execution cost models (Almgren-Chriss, propagator models), and working knowledge of optimal execution theory .
• Advanced quantitative training — MSc/PhD in applied mathematics, statistics, CS, or financial engineering from a top-tier institution.

Preferred Edge:

• Experience running delta-neutral, cross-sectional, or market-neutral books , across APAC, US, or global hours.
• Familiarity with multi-model ensemble frameworks , feature pipelines, and online learning applications.
• Demonstrated ability to manage drawdown and regime-specific tail risk using real-time diagnostics and alpha/risk overlays.
• Understanding of exchange microstructure in major venues (CME, Eurex, HKEX, SGX, Nasdaq, LSE).

Strategic capital allocation based on signal quality, strategy orthogonality, and turnover constraints.

• Performance-aligned payout structure with potential for P&L share, team lift-outs, or principal platform structures .
• True autonomy in research and execution, with collaborative support from engineering, quant dev, and TCA teams.