About Millennium
Millennium is a global investment management firm built on a scalable, technology-driven platform. We run a diverse set of investment strategies and empower our teams to deliver exceptional outcomes by providing world-class tools, infrastructure, and data.
Team & Role Overview
The Latency-Critical Trading team is building a best‑in‑class systematic data platform to power the next generation of low‑latency systematic strategies. The team includes low‑latency Linux, network, datacenter, and C++ engineers focused on our end‑to‑end trading stack.
Key Responsibilities
Monitor and assess the quality of live and historical market data; detect, inventory, and remediate data gaps.
Maintain and document exchange session times, holiday schedules, timestamp rules, and protocol/microstructure changes.
Analyze latency, data rates, bursts, and message flows to understand microstructure behavior and system performance.
Clean, transform, and manage an inventory of large‑scale datasets (including PCAP/PCAPNG) in a hybrid cloud/on‑prem environment.
Build and improve tools for market data capture.
Work with vendors and brokers to assess and provision datasets.
Build and improve tools for data analysis, visualization, and diagnostics on top of captured market and network data.
Enhance and extend C++ analytics libraries and expose them within a Python environment for systematic research and alpha development.
Collaborate closely with portfolio managers, quantitative researchers, and engineers to translate trading use cases into robust data and tooling solutions.
Qualifications
Bachelor’s or Master's in Computer Science, Mathematics, Statistics, Engineering, or another quantitative field, or equivalent experience.
3+ years of experience in financial markets, electronic trading, or high‑frequency / systematic environments preferred.
Strong programming skills in Python, C++, and SQL; experience with R / MATLAB / SciPy stack / PyTorch or similar tools for data analysis.
Solid understanding of modern statistical testing methods and comfort working with large, noisy, real‑world datasets.
Experience with Linux, large‑scale data processing, and preferably network data (PCAP, timestamping, PTP) and low‑latency systems.
Strong problem‑solving skills, attention to detail, and effective communication with both technical and non‑technical stakeholders.