Quant Researcher: Data-Driven Equity Signals & Strategy

LGBT Great; New York (NY); Hybrid; USD 120,000 - 130,000; Full time

Benefits offered by this job

Mankind days

Flexible working arrangements

Job summary

Man Group is seeking a quantitative researcher to develop and improve investment strategies in equity markets. You will work across the full research lifecycle, from idea generation and data sourcing to model building and trade execution, contributing to a broader research agenda.

The role requires a strong background in statistics/econometrics, extensive data analysis, and solid programming in Python, with collaboration across teams and a track record of original research.

Qualifications

• Advanced degree in Computer Science, Statistics, Mathematics, Finance/Economics or related quantitative field.
• Track record of original research and demonstrated problem solving ability.
• Excellent quantitative skills; econometrics or statistics with empirical research experience.
• Strong programming skills (Python preferred).
• Confident communicator; good interpersonal skills to work with the team.
• Self-organized; able to manage multiple projects and priorities.
• 3+ years of experience in research; prior investment field experience is a plus.

Responsibilities

• Develop and improve quantitative investment strategies in equity markets across the research lifecycle from idea generation to trade execution.
• Identify new investment ideas or data sources and refine data for modeling.
• Code and perform statistical/ML analysis to build and test predictive models.
• Interpret and present results; implement insights into strategies.
• Collaborate with adjacent research teams on data, techniques and insights.

Skills

Python

Data analysis

Econometrics

Statistics

Communication

Project management

Education

Advanced degree in CS/Statistics/Math/Finance

Job description

Man Group is seeking a quantitative researcher to develop and improve investment strategies in equity markets. You will work across the full research lifecycle, from idea generation and data sourcing to model building and trade execution, contributing to a broader research agenda.

The role requires a strong background in statistics/econometrics, extensive data analysis, and solid programming in Python, with collaboration across teams and a track record of original research.