Quantitative Researcher Systematic Equities
Full Time
full-time
Dubai
Verified by Turrior
Content + Source + Freshness • 11 Dec 2025 • 95% confidence
83 / 100
Offer value
The role scores highly due to its potential for involvement in innovative trading strategies, the opportunity to leverage machine learning, and a solid compensation structure.
- Involvement in data-driven systematic trading
- Opportunity to work with industry-leading minds
- Develop machine learning-driven insights for trading
Pros
- Engagement in high-level quantitative research at a leading hedge fund
- Direct collaboration with experienced portfolio managers
- Strong prospects for advancement in a rapidly evolving field
Cons
- Requires entry into a demanding sector with high expectations
- Candidate must handle substantial data complexities
- Work is likely to involve tight deadlines and competitive pressure
Who it's for
Mid to Senior Level • Flexible with potential remote work
Good fit
- Experienced quantitative researchers
- Data scientists interested in finance
- Professionals with a passion for statistical modeling
Not recommended for
- New candidates without relevant experience
- Individuals averse to a fast-paced work environment
- Those who prefer less complex problem-solving scenarios
Motivation fit
Desire to develop systematic trading strategiesInterest in working with large datasets and innovative modelsAspiration toward professional growth in finance and technology
Key skills
Machine learning techniquesStatistical analysisData manipulation and cleaningFinancial modeling
Score: 83/100 AI verified analysis
About the job
Job Description:
Job Description: Quantitative Researcher, Systematic EquitiesPlease 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:
- Idea generation
- 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)
- 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
- 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
- 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
Requirements:
