Quantitative Researcher, Systematic Equities

Company: Quant Blueprint LLC
Apply for the Quantitative Researcher, Systematic Equities
Location: London
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.

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Posted: June 3rd, 2026