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 bestinclass systematic data platform to power the next generation of lowlatency systematic strategies. The team includes lowlatency Linux, network, datacenter, and C++ engineers focused on our endtoend 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 largescale datasets (including PCAP/PCAPNG) in a hybrid cloud/onprem 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 highfrequency / 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, realworld datasets.
- Experience with Linux, largescale data processing, and preferably network data (PCAP, timestamping, PTP) and lowlatency systems.
- Strong problemsolving skills, attention to detail, and effective communication with both technical and nontechnical stakeholders.
