Quant Researcher: Nonlinear Signals, Regime ML Trading

Trading Interview; New York (NY); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Medical, dental, vision

401k with match

Short/long-term disability

Life/AD&D

HSA

FSA

Job summary

DRW is seeking a Quantitative Researcher with 3–5 years of experience to develop nonlinear signal modeling, regime-aware frameworks, and the integration of new data sources into existing research and risk systems. The role involves close collaboration with traders and quants, with direct impact on live trading.

The ideal candidate has a strong ML and statistics background, a Masters or PhD, and is proficient in Python with time-series data experience.

Qualifications

• 3+ years of experience in quantitative research, systematic trading, or applied ML in financial markets.
• Masters/PhD in a quantitative field required.
• Proficiency in Python and common data/ML libraries.
• Experience working with time-series data and building predictive models.
• Strong analytical skills and ability to communicate results to trading teams.

Responsibilities

• Design non-linear signal combination frameworks and improve upon linear approaches.
• Build regime-aware models and conditional signal frameworks based on market states.
• Research, develop, and validate ML-based trading signals on intraday and daily data.
• Integrate signals into risk sizing, portfolio construction, and optimization frameworks.
• Work with traders and infrastructure teams to transition research into production.

Skills

Machine learning

Time-series analysis

Quantitative research

Python

Statistical modeling

Education

Masters/PhD in quantitative field

Tools

Pandas

NumPy

scikit-learn

Job description

DRW is seeking a Quantitative Researcher with 3–5 years of experience to develop nonlinear signal modeling, regime-aware frameworks, and the integration of new data sources into existing research and risk systems. The role involves close collaboration with traders and quants, with direct impact on live trading.

The ideal candidate has a strong ML and statistics background, a Masters or PhD, and is proficient in Python with time-series data experience.