Quantitative Researchers, often called 'quants,' apply advanced mathematics, statistics, and computer science to analyze financial markets and develop trading strategies. They work at hedge funds, investment banks, proprietary trading firms, and asset managers, building models that predict price movements, manage risk, and identify profitable opportunities. Their work blends deep theoretical knowledge with practical coding skills, as models must be rigorously backtested and implemented in fast, reliable systems.
| Entry level | $130,000 |
| Median | $225,000 |
| Senior | $450,000 |
| Top 10% | $1,000,000 |
| Job growth | +23% |
| Professionals in the USA | 0.06 million |
| Typical hours/week | 55 hrs |
| Remote work share | 20% |
| Annual job openings | 5,500/yr |
| Demand | High |
AI and machine learning tools are transforming quantitative research by automating data processing, backtesting, and initial signal generation. However, the core intellectual work of designing novel strategies, understanding market microstructure, and managing risk under uncertainty still requires human judgment. Quants who leverage AI as a tool rather than compete with it will thrive.
Automation exposure: Routine data cleaning, feature engineering, standard backtesting, report generation, and basic statistical modeling are increasingly automated by AI-driven pipelines and auto-ML tools.
The human edge: Creative hypothesis generation, understanding regime changes and market psychology, ethical judgment on risk-taking, cross-domain intuition, and the ability to question model assumptions in novel or crisis conditions remain distinctly human strengths.
Figures are estimates for exploration — verify current data with BLS.gov.