Quantitative Developer, Systematic Equities

Quant Blueprint LLC; Hong Kong; On-site; HKD 80,000 - 120,000; Full time

Job summary

Quant Blueprint LLC is looking for a Quantitative Researcher specializing in systematic equities. You will collaborate with the Senior Portfolio Manager to develop strategies grounded in data analytics and machine learning. The ideal candidate has over 3 years of experience in a trading environment and a strong background in data science tools and quantitative finance.

The role is based in Hong Kong but may also consider candidates from London or Dubai. Interested candidates should have a solid grasp of Python and experience with handling complex datasets.

Qualifications

• 3+ years of experience working in a systematic trading environment focused on equities.
• Experience with multiple vendor data sets: assessing, cleaning, and creating features.
• Demonstrated knowledge of machine learning concepts with hands-on experience.

Responsibilities

• Develop systematic trading strategies with a focus on idea generation and data analysis.
• Harness large financial datasets using statistical learning techniques.
• Implement machine learning frameworks and optimize code for scalability.

Skills

Proficient in modern data science tools (Jupyter, pandas, numpy, sklearn)

Expert in Python

Strong analytical skills

Experience with machine learning

Knowledge of quantitative finance

Education

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

Tools

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.