PhD Quantitative Researcher: High-Frequency Trading Signals

Teza Technologies; Austin (TX); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Health insurance

Flexible sick time policy

Office Lunches

Job summary

Teza Technologies in Austin, TX is seeking exceptional quantitative researchers to develop systematic trading strategies based on market microstructure. You will extract predictive signals from high-frequency data and turn them into scalable trading models, exploring large datasets, testing hypotheses, and deploying ideas into production.

The role focuses on building robust research infrastructure and collaborating with experienced researchers and Portfolio Managers to move ideas from hypothesis

Qualifications

• PhD in Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, or related quantitative field.
• Excellent mathematical and statistical skills.
• Strong programming ability in Python and C++/Java.
• Experience working with quantitative models.
• Curiosity, creativity, and proven academic track record.

Responsibilities

• Research predictive signals from market microstructure data.
• Design and evaluate new features using the most granular market data.
• Develop statistical and machine learning models for systematic trading.
• Build robust research infrastructure and analytical tools.
• Collaborate with researchers and portfolio managers to move ideas to live trading.

Skills

Python

C++/Java

Quantitative models

Research

Education

PhD in Mathematics/Statistics/Physics/CS/EE

Job description

Teza Technologies in Austin, TX is seeking exceptional quantitative researchers to develop systematic trading strategies based on market microstructure. You will extract predictive signals from high-frequency data and turn them into scalable trading models, exploring large datasets, testing hypotheses, and deploying ideas into production.

The role focuses on building robust research infrastructure and collaborating with experienced researchers and Portfolio Managers to move ideas from hypothesis