Quantitative Developer/System Engineer, Systematic Equities

Quant Blueprint LLC; Singapore; On-site; SGD 89,502 - 127,861; Full time

Job summary

Quant Blueprint LLC is seeking a Quantitative Researcher in Singapore to develop systematic trading strategies in global equities. You will be responsible for evaluating financial datasets, implementing machine learning frameworks, and collaborating closely with the Senior Portfolio Manager.

The ideal candidate has a strong background in data science, with excellent analytical skills and familiarity with statistical analysis. A Bachelor's or Master's in a relevant field is a must, along with experience in a systematic trading environment.

Qualifications

• 3+ years of experience in a systematic trading environment focused on equities.
• Experience with vendor data sets, including assessing, cleaning, and creating features.
• Hands-on experience applying machine learning in financial contexts.

Responsibilities

• Develop systematic trading strategies with a focus on data analysis.
• Analyze large financial datasets using statistical techniques.
• Optimize and implement machine learning frameworks.

Skills

Data science tools (Jupyter, pandas, numpy, sklearn)

Machine learning experience

Python expertise

Statistical analysis and mathematical modeling

Communication and problem-solving skills

Education

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