Teza Technologies
Teza Technologies

Quantitative Researcher, PhD

TLDR

Develop predictive signals from market microstructure data at high-frequency to build scalable trading models.


We are looking for exceptional quantitative researchers to develop systematic trading strategies based on market microstructure.

This role is focused on extracting predictive signals from high-frequency market data and turning them into robust, scalable trading models. You will explore large datasets, develop new features, test hypotheses, and work closely with researchers and engineers to deploy ideas into production.


Location

Austin, TX (5 days in-office requirement)


Key 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.

  • Work directly with experienced researchers and Portfolio Managers to take ideas from hypothesis to live trading.


Basic Requirements

  • PhD in Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, or a related quantitative discipline.

  • Outstanding mathematical and statistical skills.

  • Strong programming ability in Python and C++/Java.

  • Experience working with quantitative models.

  • Curiosity, creativity, and proven academic track record.


What you’ll get

  • On-site presence of experienced Quantitative Researchers and Portfolio Managers to learn from

  • Build Strategies while becoming the best at what you do

  • Professional guidance from experienced mentors


Benefits

  • Health insurance

  • Flexible sick time policy

  • Office Lunches

Benefits

Flexible Work Hours

Flexible sick time policy

Office lunches

Health Insurance

Teza Technologies is a systematic trading firm that develops quantitative strategies across various asset classes. Our focus is on leveraging data and advanced algorithms to drive trading decisions, catering to the financial market's evolving demands.

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