Quantitative Researcher | Trading Team

Jump Trading•New York, NY

7h•$250,000 - $300,000•Remote

About The Position

Jump Trading Group is committed to world-class research, empowering exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique, fostering constant innovation through fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. We bring together world-class talent, battle-tested infrastructure, and serious research intensity to build and scale trading strategies across every asset class and time horizon. Equities is a competitive, global, and fast-growing asset class. Researchers coalesce around projects that may be market specific, latency specific, or research approach specific. Our environment is collaborative, seeking to balance deep focus and expertise with the freedom to chase ideas across boundaries. We are looking for experienced Quantitative Researchers to join a “mixed frequency” research group (horizons range from many minutes to many days). It’s a flat, fast-moving, collaborative environment where each member has agency, and of whom much is expected. The team is global as is the research.

Requirements

Creativity: Seeing things where others do not requires creativity and a willingness to try new things in ways others may not have thought of.
Resilience: Successful research is the result of lots of failure, and intellectual risk taking.
STEM Research foundations: Undergraduate and graduate work in Computer Science, Statistics, Physics, Mathematics (or adjacent).
Expertise in C++ and Python (either works, and both is better).
Applied programming skills in data set breakdown and machine learning.
Prior experience in stat arb equities: all disciplines in new feature creation, portfolio construction and optimization, and execution are relevant and attractive.
Prior work in portfolio trading.
Experience in a direct market access environment.

Responsibilities

Collect and analyze tens of thousands of data sets (some clean, some noisy, some very noisy).
Identify patterns and develop insights into market complexities.
Develop forecasts to be realized in equity markets.