Quantitative Researcher, Systematic Equities

Millennium


Date: 2 weeks ago
City: Dubai
Contract type: Full time
Job Description: Quantitative Researcher, Systematic Equities

Please direct all resume submissions to [email protected].

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)
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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