Quantitative Researcher

AAA Global; Gurugram District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

AAA Global is seeking exceptional scientists, engineers, economists, and programmers to help develop machine learning models for predicting price movements across global financial markets. The role involves designing, testing, and deploying systematic trading strategies within a research-driven environment.

Applicants should possess strong Python programming skills, a background in STEM, and 1–10 years of experience in quantitative research or alpha trading signal development.

Qualifications

• Bachelor's, Master's, or PhD in a STEM discipline from a prestigious institution.
• Strong interest in quantitative finance and machine learning.
• Excellent analytical and problem-solving skills.
• Ability to work independently as well as collaboratively in a research-driven environment.
• Strong programming skills in Python.
• Prior experience in alpha research and the development of quantitative trading signals or systematic strategies.
• 1–10 years of relevant experience in quantitative research, machine-learning, or systematic trading.

Responsibilities

• Design, implement, and optimize machine learning models for predicting liquid financial assets using diverse financial datasets and a broad library of trading signals.
• Analyze and process large datasets for alpha research and strategy development.
• Research and implement cutting-edge academic work in quantitative finance and machine learning.
• Collaborate with experienced quantitative researchers to develop, test, and validate trading hypotheses through rigorous simulations.
• Contribute to the research, development, and deployment of systematic trading strategies.

Skills

Python

Analytical thinking

Problem solving

Machine learning

Quantitative finance interest

Education

Bachelor's/Master's/PhD in STEM

Job description

Our client is a leading systematic hedge fund that develops and deploys thousands of statistical algorithms to trade global equity and futures markets. By leveraging extensive financial datasets, advanced machine learning techniques, and quantitative research, the firm builds market-neutral trading strategies designed to identify and capitalize on inefficiencies across liquid financial markets.

The client is seeking exceptional scientists, engineers, economists, and programmers to help develop the next generation of machine learning models capable of predicting price movements across global financial markets.

Responsibilities

• Design, implement, and optimize machine learning models for predicting liquid financial assets using diverse financial datasets and a broad library of trading signals.
• Analyze and process large datasets for alpha research and strategy development.
• Research and implement cutting-edge academic work in quantitative finance and machine learning.
• Collaborate with experienced quantitative researchers to develop, test, and validate trading hypotheses through rigorous simulations.
• Contribute to the research, development, and deployment of systematic trading strategies.

Requirements

• Bachelor's, Master's, or PhD degree in a STEM discipline from a prestigious institution.
• Strong interest in quantitative finance and machine learning.
• Excellent analytical and problem-solving skills.
• Ability to work independently as well as collaboratively in a research-driven environment.
• Strong programming skills in Python.
• Prior experience in alpha research and the development of quantitative trading signals or systematic strategies.
• 1–10 years of relevant experience in quantitative research, machine-learning, or systematic trading.

What’s on Offer

• Highly competitive compensation with performance-based bonuses linked to the success of the strategies you develop.
• A collaborative, research-focused environment with direct exposure to experienced quantitative researchers.
• The opportunity to influence research direction, contribute innovative ideas, and take ownership of new initiatives.
• Exposure to cutting-edge machine learning techniques and large-scale quantitative research infrastructure.