Quantitative Researcher, Systematic Equities
Posted July 30, 2025
Job Description
Quantitative Researcher, Systematic Equities
Job Description: Quantitative Researcher, Systematic Equities
*Please direct all resume submissions to QuantTalentEUR@mlp.com. *
Millennium is a top tier global hedge fund with a strong commitment 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
Principal Responsibilities
- Work alongside the Senior Portfolio Manager on developing systematic trading strategies, with a primary focus on:
- Data gathering and research/analysis
- Model implementation and back testing for systematic global equities strategies
- Explore, analyze, and harness large financial datasets using a variety of statistical learning techniques Work with multiple vendor data sets: assessing, cleaning, creating features
- Implement flexible, scalable and efficient machine learning framework using existing features Optimize code for larger scale work
- Create new features using additional database (KDB preferred)
Preferred Technical Skills
- Proficient in modern data science tools stacks (Jupyter, pandas, numpy, sklearn) with machine learning experience
- Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from top ranked University
- Expert in Python (KDB/Q is a plus)
- Demonstrated knowledge of quantitative finance, mathematical modelling, statistical analysis, regression, and probability theory
- Excellent communication, problem-solving, and analytical skills, with the ability to quickly understand and apply complex concepts
Preferred Experience
- 3+ years of experience working in a systematic trading environment with a focus on equities
- 3+ years of experience working with multiple vendor data sets and, in particular, manipulating data (assessing, cleaning, creating features, etc.)
- Demonstrated theoretical understanding of Machine Learning with 2-3+ years of hands-on experience in the applications
- Experience collaborating effectively with cross functional teams, multitasking and adapting in a fast-paced environment
Highly Valued Relevant Attributes
- Strong intuition about feature/data prediction power
- Extremely rigorous, critical thinker, self-motivated, detail-oriented, and able to work independently in a fast-paced environment
- Entrepreneurial mindset
- Curiosity and eagerness to learn and grow professionally
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