Quant jobs / DRW

Quantitative AI Strategist

A front-office role in the quantitative analytics team, building an AI-powered research and analytics platform used by traders, researchers and risk managers. You turn desk needs into quantitative workflows with engineers.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Work with trading desks to define quantitative use cases for the platform
Prototype signal, backtest, PnL simulation and risk workflows
Write libraries and interfaces designed for AI agents to call
Evaluate platform performance and drive improvements
Give first-line quantitative support to users

What they ask for

Required

3-7 years as a front-office quant, strategist or researcher
Financial markets, pricing and risk methods, PnL attribution
Signal generation and backtesting
Strong Python and Git
Building internal analytics tools
Communication with trading desks

Preferred

AI technologies applied to quantitative workflows
Building AI agents

Education: Quantitative finance, financial engineering, applied maths, statistics, physics, computer science or similar; level not stated
Experience: 3-7 years in a front-office quant, strategist or quant research role
Languages and tools: Python
Markets: multi-asset

How to prepare

Build a backtest workflow that an LLM agent can drive through well-documented functions
Learn PnL attribution and risk scenario analysis
Practise turning vague desk requests into quantitative specs
Experiment with LLM agent frameworks

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on DRW’s site before applying: requirements, location and pay can change after we check them.

View the posting on DRW’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with DRW and doesn’t handle applications.