Quantitative Execution Trader, Systematic Equities

Quant Blueprint LLC; Hong Kong; On-site; HKD 626,959 - 940,438; Full time

Job summary

Quant Blueprint LLC is seeking a Quantitative Researcher with expertise in systematic equities trading. This role involves working alongside the Senior Portfolio Manager to develop innovative trading strategies, focused on analyzing financial datasets and machine learning implementations.

The ideal candidate should possess a strong background in data science with over 3 years of relevant experience in systematic trading, demonstrated proficiency in Python, and a degree in a relevant STEM field. The position may be based in Hong Kong, London, or Dubai.

Qualifications

• 3+ years of experience in systematic trading, focusing on equities.
• Hands-on experience with data manipulation and feature engineering.
• Demonstrated knowledge of machine learning and statistical techniques.

Responsibilities

• Develop systematic trading strategies with a focus on idea generation and data analysis.
• Analyze and clean large financial datasets.
• Implement scalable machine learning frameworks.

Skills

Proficient in modern data science tools

Expert in Python

3+ years of experience in systematic trading

Statistical analysis and mathematical modelling

Education

Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field

Tools

Jupyter

pandas

numpy

sklearn

KDB/Q

Job description

Quantitative Researcher, Systematic Equities

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 analysis
  • Model implementation and back testing for systematic global equities strategies
• Explore, analyze, and harness large financial datasets using various 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.