Quantitative Developer/System Engineer, Systematic Equities

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:
  • 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