Quantitative Researcher, Systematic Equities
Millennium Management
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
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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
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Explore, analyze, and harness large financial datasets using a variety of statistical learning techniques
Work with multiple vendor data sets: assessing, cleaning, creating features
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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