Crypto D1 Quant Trader: Live PnL & Automated Execution

BlockTech; Singapore; On-site; SGD 180,000 - 320,000; Full time

Benefits offered by this job

Relocation package

Company laptop

Home office budget

Transportation allowance

Learning budget

Gym membership reimbursement

Daily meals

Job summary

BlockTech’s Singapore trading floor seeks a Quantitative Trader responsible for end-to-end PnL ownership, interpreting live market flow, and tuning automated systems. You will shape execution logic and build tools with researchers, developers, and engineers.

Requirements include 3+ years in quantitative trading or research with PnL responsibility, hands-on experience with live automated systems, strong data analytics, and proficiency in Python. English communication is essential.

Qualifications

• 3+ years of experience as a Quant Trader and/or Quantitative Researcher with PnL responsibility on linear (D1) products across multiple venues
• Hands-on experience with automated/semi-automated trading systems under live conditions
• Strong analytical instincts to turn fills/quotes into execution improvements
• Deep grasp of market microstructure and ability to explain why a strategy works

Responsibilities

• Manage and monitor D1 positions across multiple venues, owning PnL and risk
• Read live market flow and translate into trading/execution decisions
• Tune and parameterise automated systems and intervene to manage risk or capture opportunities
• Improve execution quality and market-making performance through data-driven analysis
• Design and specify trading tools, dashboards, and analytics used by the desk
• Collaborate with researchers, developers, and engineers to sharpen models and execution logic

Skills

Python

PnL ownership

Market microstructure

Data analysis

English communication

Trading systems

Proactive ownership

Tools

Automated trading systems

Job description

BlockTech’s Singapore trading floor seeks a Quantitative Trader responsible for end-to-end PnL ownership, interpreting live market flow, and tuning automated systems. You will shape execution logic and build tools with researchers, developers, and engineers.

Requirements include 3+ years in quantitative trading or research with PnL responsibility, hands-on experience with live automated systems, strong data analytics, and proficiency in Python. English communication is essential.