Investment Associate

Bridgewater Associates•New York, NY

8h•$225,000 - $450,000

About The Position

For 50 years, Bridgewater has pursued one idea: the world can be understood. Markets and economies follow cause-and-effect relationships—and by understanding them, we think we can beat the markets and generate true uncorrelated returns ("alpha") at significant scale. Our clients include some of the world’s most sophisticated institutional investors who turn to us to use our unique insights to solve their biggest problems. Generating alpha at scale is an exceptionally difficult task. It requires predicting the future and doing so better than the millions of other extremely smart, highly motivated people who are trying to do the same. Our investment strategies seek to understand and navigate macroeconomic shifts that drive the world’s most liquid markets (bonds, currencies, equities, commodities, credit). These shifts have a very limited sample size (some of them haven’t yet happened in our lifetimes). Our approach is to start by digging deep to develop a fundamental cause-effect understanding of the economic and financial relationships that drive markets. We represent this understanding in a model of the world that we call our System—code and algorithms that generate views automatically, by ingesting vast amounts of significantly cleaned data and reflecting the relationships we’ve learned over decades of intense study and experience. Being systematic allows us to stress-test the quality of our ideas through time, and also helps ensure that at any given point, our positions reflect everything we’ve ever learned, so that our people can spend all their time focused on compounding on our understanding at a faster rate than markets are learning. This approach requires us to be at the forefront of human-machine collaboration. Since 2012, Bridgewater has aggressively pursued the vision of the artificial investor that can do everything a human can, with computers not just representing the insights but also generating the insights themselves. In 2023, we introduced AIA (Artificial Investment Associate), our fully machine-powered investing strategy. Our learnings in building AIA are now transforming even our human investors’ jobs, allowing us to rapidly discover and systemize new insights, and accelerating our transformation toward a fully integrated system that combines the best of human and machine intelligence. Every day we obsessively interrogate our systems against our independent investor insights, and work to evolve our systems to reflect how the world is changing. Beating the markets with this approach requires intense collaboration between brilliant people who constantly push themselves and each other to improve every week, to arrive at the best ideas without ego or politics. We rapidly elevate the best thinkers to greater responsibility. We are looking to hire great talent for our Investment Associate roles. Does this sound like you? Investment Associates sit at the frontier of how Bridgewater learns and invests. They are equal parts investor, engineer, and inventor—building and evolving the systems that power one of the world's largest hedge funds. We are looking for people with backgrounds in quantitative fields—computer science, machine learning, mathematics, statistics, physics, engineering—who have spent a few years building real systems and now want to apply that capability to understanding how economies and markets work and translating that understanding into systematized investment decisions.

Requirements

A strong foundation in a quantitative or technical discipline—computer science, machine learning, mathematics, statistics, physics, engineering, or a related field
A few years of professional experience building systems, models, or quantitative tools—whether in technology, finance, research, or another demanding environment
A problem-solving orientation: analytically rigorous and detail-oriented, but able to flexibly learn new methods to get from concept to execution
Genuine intellectual passion for understanding how economies and markets work—not just modeling them, but understanding the causal mechanics
Fluency in at least one programming language commonly used in quantitative research or systems development