Quantitative Researcher: Regime-Aware ML Trading Signals

DRW; New York (NY); Vor Ort; USD 150.000 - 250.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Dental insurance

Vision insurance

401k with employer match

Disability insurance

Zusammenfassung

DRW is seeking a Quantitative Researcher with 3–5 years of experience to develop and improve systematic trading signals using machine learning and statistical methods. You will design non-linear signal frameworks, build regime-aware models, and work closely with traders to transition research into production.

You will integrate signals into risk sizing and portfolio construction, validate ML-based signals on intraday and daily data, and contribute to production-ready research in a fast-paced

Qualifikationen

• 3+ years in quantitative research or ML in finance.
• Masters/PhD required.
• Proficiency in Python and data libraries.
• Experience with time-series data and predictive models.
• Strong analytical skills and ability to communicate results to trading teams.

Aufgaben

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

Kenntnisse

ML basics

Statistics

Time-series

Python

Trader communication

Ausbildung

Masters or PhD in relevant field

Jobbeschreibung

DRW is seeking a Quantitative Researcher with 3–5 years of experience to develop and improve systematic trading signals using machine learning and statistical methods. You will design non-linear signal frameworks, build regime-aware models, and work closely with traders to transition research into production.

You will integrate signals into risk sizing and portfolio construction, validate ML-based signals on intraday and daily data, and contribute to production-ready research in a fast-paced