AI/ML Quant Trader

DTG Finance & Capital Markets; United States; On-site; USD 140,000 - 230,000; Full time

Job summary

DTG Finance & Capital Markets seeks a highly skilled researcher-designer for AI-driven trading systems. The role focuses on researching signals from market and alternative data, and building systematic strategies where ML drives decisions rather than discretionary trading.

You will design, test, deploy, and improve the trading system, running large-scale backtests and live deployments while monitoring performance across regimes.

Qualifications

• Master's degree or higher in a quantitative field (CS/Math/Statistics/Finance).
• Strong background in ML with applications to time-series data.
• Experience turning research into production trading systems.

Responsibilities

• Researching signals using market and alternative datasets.
• Designing systematic trading strategies driven by ML outputs.
• Running large-scale backtests and robustness checks.
• Deploying models into live trading systems and monitoring performance.

Skills

Machine Learning

Systematic Trading

Quantitative Research

Financial Time-Series Data

Research & Production Code

Python

Education

Master's degree in CS/Math/Statistics/Finance

Tools

Python

Pandas

NumPy

Job description

Fully Systematic Trading | Machine Learning Driven

What this role really is

A trader who builds machines that trade.

Not discretionary trading enhanced by models — but fully systematic strategies where AI and ML drive the decision process, from signal generation to portfolio construction.

You design the system, test it, deploy it, and improve it.

The core idea

Markets are noisy, adaptive systems.

Machine learning can help uncover patterns — if applied carefully.

What you’ll actually be doing

Researching signals

• Mining market and alternative datasets for predictive structure
• Applying ML techniques to forecasting, classification, and regime detection

Building strategies

• Designing systematic trading strategies driven by model outputs

Running experiments

• Running large-scale backtests and robustness checks
• Stress-testing models across regimes and market conditions

Operating live systems

• Deploying models into production
• Monitoring performance and refining strategies over time

What your background might look like

You likely have experience somewhere along the intersection of:

• machine learning
• systematic trading
• quantitative research
• built models that actually traded
• worked with financial time-series data
• written a lot of research and production code

Programming (usually Python) and strong statistical intuition are essential.

How success is measured

• quality of signals
• robustness of strategies
• live trading performance

Why people take roles like this

• Full ownership of systematic strategies
• Ability to apply modern ML methods to markets
• Research that directly translates into capital allocation
• Opportunity to scale successful strategies

In short:

This role is for someone who enjoys designing and running AI-driven trading systems — not just researching them.