Our client, a top-tier systematic fund, is seeking a
Quantitative Developer
to join its centralized fund team. This role is pivotal in bridging the gap between sophisticated mathematical research and high-performance execution. You will be responsible for the end-to-end development of the trading lifecycle, from designing robust backtesting engines to optimizing production-grade execution algorithms.
Core Responsibilities
Strategy Engineering:
Architect, implement, and deploy complex quantitative strategies and execution logic primarily using Python.
Infrastructure Development:
Build and maintain high-fidelity backtesting environments and research frameworks capable of handling high-frequency market data.
Research Partnership:
Collaborate directly with PM and Quantitative Researchers to translate theoretical models into high-performance, production-grade code, ensuring zero-drift between simulation and live execution.
Performance Optimization:
Profile and tune system components for maximum throughput, low latency, and efficient memory management to maintain a competitive edge in fast-moving markets.
Data Pipeline Engineering:
Design and manage scalable pipelines for processing vast datasets (tick-by-tick and alternative data) to empower both research and real-time trading operations.
Requirements \& Qualifications
Professional Experience
Tenure:
3–5 years of hands-on experience in a Python Quantitative Development role.
Industry Background:
Proven track record within a top-tier global hedge fund, proprietary trading firm, or quantitative investment manager.
Delivery:
Demonstrated success in shipping mission-critical trading software and supporting live production environments.
Technical Skill Set
Python Expertise:
Mastery of the Python ecosystem (
NumPy, Pandas
) for high-performance data analysis and research tooling.
System Programming:
Deep understanding of multi-threaded programming, network protocols (
TCP/UDP
), and
Linux kernel/
system internals.
Data Architecture:
Familiarity with high-performance time-series databases (e.g.,
kdb+/q
) and modern SQL/NoSQL storage solutions.
Quantitative \& Market Acumen
Market Sense:
A solid understanding of the quantitative strategy lifecycle, including signal generation, portfolio construction, and market impact.
Strategy Exposure (Preferred):
Prior experience with
Statistical Arbitrage
or
Event-Driven
strategies is highly desirable, including an understanding of the specific data and execution nuances required for these styles.
Educational Background
Academic Excellence:
Bachelor’s, Master’s, or PhD from a leading university in
Computer Science, Mathematics, Physics, Engineering,
or a related quantitative discipline.