Quantitative Researcher, Systematic Equities.

Quantitative Researcher, Systematic Equities

Job Description :

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.

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

Responsibilities:

1. Work alongside the Senior Portfolio Manager on developing systematic trading strategies, with a primary focus on:
2. Data gathering and research/analysis
3. Model implementation and back testing for systematic global equities strategies
4. Explore, analyze, and harness large financial datasets using a variety of statistical learning techniques
5. Work with multiple vendor data sets: assessing, cleaning, creating features
6. Implement flexible, scalable and efficient machine learning framework using existing features
7. Optimize code for larger scale work
8. Create new features using additional database (KDB preferred)

Preferred Technical Skills:

1. Proficient in modern data science tools stacks (Jupyter, pandas, numpy, sklearn) with machine learning experience
2. Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from a top-ranked University
3. Expert in Python (KDB/Q is a plus)
4. Demonstrated knowledge of quantitative finance, mathematical modelling, statistical analysis, regression, and probability theory
5. Excellent communication, problem-solving, and analytical skills, with the ability to quickly understand and apply complex concepts

Preferred Experience:

1. 3+ years of experience working in a systematic trading environment with a focus on equities
2. 3+ years of experience working with multiple vendor data sets and, in particular, manipulating data (assessing, cleaning, creating features, etc.)
3. Demonstrated theoretical understanding of Machine Learning with 2-3+ years of hands-on experience in the applications
4. Experience collaborating effectively with cross-functional teams, multitasking and adapting in a fast-paced environment

Highly Valued Relevant Attributes:

1. Strong intuition about feature/data prediction power
2. Extremely rigorous, critical thinker, self-motivated, detail-oriented, and able to work independently in a fast-paced environment
3. Curiosity and eagerness to learn and grow professionally