Quantitative Researcher, PhD

Teza Technologies


Job Location:

Austin, TX - USA

Monthly Salary: Not Disclosed
Posted on: 4 days ago
Vacancies: 1 Vacancy

Job Summary


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 youll 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


Required Experience:

IC

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 ...

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