Quantitative Researcher, Systematic Equities.

Millennium Management; Dubai; On-site; USD 80,000 - 120,000; Full time

Job summary

A leading global hedge fund seeks a Quantitative Researcher for systematic trading in Dubai. The role involves developing trading strategies, analyzing financial data, and implementing machine learning frameworks. Ideal candidates will have a strong STEM background, experience in systematic trading, and proficiency in data science tools.

Qualifications

• 3+ years in a systematic trading environment focusing on equities.
• Hands-on experience applying machine learning theories (2-3+ years).
• Experience with multiple vendor datasets and data manipulation.

Responsibilities

• Develop systematic trading strategies alongside the Senior Portfolio Manager.
• Gather, research, and analyze financial data.
• Implement models and conduct back testing for global equities strategies.

Skills

Machine Learning

Statistical Analysis

Data Manipulation

Problem Solving

Communication

Education

Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field

Tools

Python

Jupyter

pandas

numpy

sklearn

Job description

Quantitative Researcher, Systematic Equities

Job Description : Quantitative Researcher, Systematic Equities

Millennium is a top-tier global hedge fund committed to leveraging market innovations in technology and data to deliver high-quality returns.

Job Description

We are seeking a quantitative researcher to partner with the Senior Portfolio Manager to implement a machine learning research framework for the systematic trading of global equity strategies.

Location

London or Dubai preferred

Responsibilities:

1. Develop systematic trading strategies alongside the Senior Portfolio Manager.
2. Gather, research, and analyze financial data.
3. Implement models and conduct back testing for global equities strategies.
4. Explore, analyze, and utilize large financial datasets using statistical learning techniques.
5. Work with multiple vendor datasets: assess, clean, and create features.
6. Implement scalable and efficient machine learning frameworks using existing features.
7. Optimize code for larger-scale processing.
8. Create new features using additional databases (KDB preferred).

Preferred Technical Skills:

• Proficient in modern data science tools (Jupyter, pandas, numpy, sklearn) with machine learning experience.
• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from a top-ranked university.
• Expert in Python (KDB / Q is a plus).
• Knowledge of quantitative finance, mathematical modeling, statistical analysis, regression, and probability theory.
• Excellent communication, problem-solving, and analytical skills to understand and apply complex concepts.

Preferred Experience:

• 3+ years in a systematic trading environment focusing on equities.
• 3+ years working with multiple vendor datasets, including data manipulation.
• Hands-on experience (2-3+ years) applying machine learning theories.
• Experience collaborating with cross-functional teams in fast-paced settings.

Highly Valued Attributes:

• Strong intuition about feature/data prediction power.
• Rigorous, critical thinker, self-motivated, detail-oriented, capable of independent work.
• Curious and eager to learn and grow professionally.