Commodities Quantitative Researcher, Systematic Global Macro

Quant Blueprint LLC; Baar; Vor Ort; CHF 90.000 - 120.000; Vollzeit

Zusammenfassung

Quant Blueprint LLC is seeking a Quantitative Researcher specialized in Systematic Equities to develop trading strategies and optimize analytical models. This role requires proficiency in Python and expertise in data science tools. Candidates should have a Bachelor's or Master's degree in a relevant STEM field and at least 3 years of experience in systematic trading environments.

Responsibilities include working with large financial datasets, implementing scalable machine learning frameworks, and collaborating with cross-functional teams to drive data-centered insights and performance. A strong intuition for data prediction and a critical thinking mindset is essential.

Qualifikationen

• 3+ years of experience in a systematic trading environment focusing on equities.
• Experience manipulating data with multiple vendor data sets.
• Theoretical understanding of Machine Learning with 2-3 years of hands-on experience.

Aufgaben

• Develop systematic trading strategies with a focus on idea generation, data analysis, and model implementation.
• Analyze large financial datasets using statistical learning techniques.
• Optimize code for large-scale work.

Kenntnisse

Python

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

Statistical analysis

Machine learning

Problem-solving skills

Communication skills

Ausbildung

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

Tools

KDB/Q

Jobbeschreibung

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.