Systematic Fast Trading Intern

Man Group•New York, NY

3d•Onsite

About The Position

Man Systematics’ Fast Trading Strategies (FTS) team develops high-Sharpe, high-frequency trading strategies across asset classes and manages a portfolio across global futures and cash markets. The team develops the fast-alpha portfolio end to end, using machine learning, event-driven and market-microstructure techniques, building a customized monetization stack, and optimizing trading and order placement with dedicated high-frequency infrastructure. This internship is for candidates interested in the New York office, which houses Man Systematics’ high-frequency alpha research, trading and infrastructure business unit. The team is hiring PhD students with strong academic achievement in highly technical degree programs for an expected 10–12-week summer internship. The role may focus on machine learning engineering (MLE) or quantitative research (QR). MLE interns work on software supporting machine-learning applications, including MLOps and research workflows. QR interns use high-frequency tick data and machine-learning algorithms to predict asset returns and turn models into trading strategies. QRs work closely with MLOps when deploying models. The team describes its culture as performance-driven, meritocratic, flat, open, transparent and collaborative, with opportunities to grow and contribute. The New York office is a modern high-rise in central Midtown near Central Park. The team also holds annual away days and research off-sites and engages with the broader research and academic community.

Requirements

Currently a PhD student in a highly technical degree program.
Strong academic achievement.
For the MLE focus: coding experience in a variety of languages, especially Python, and knowledge of machine-learning algorithms and research processes.
For the QR focus: strong programming skills in a variety of languages, especially Python.

Nice To Haves

Previous machine-learning or statistical research internship experience in finance, technology or academia is strongly preferred for the QR role.

Responsibilities

For the MLE focus: develop or work with software for machine-learning applications, including MLOps and research workflows.
For the QR focus: use high-frequency tick data and machine-learning algorithms to predict asset returns and develop profitable trading strategies.
For the QR focus: work with MLOps to deploy models.

Benefits

Compensation includes a discretionary bonus in addition to base salary.
Professional development opportunities.
Flexible working arrangements are available.
Two annual paid Mankind days for community volunteering.
Competitive holiday entitlements.
Pension/401(k).
Life and long-term disability coverage.
Group sick pay.
Enhanced parental leave.
Long-service leave.
Depending on location, private medical coverage, discounted gym membership options and pet insurance may be available.