Quantitative Researcher — Data-Driven Equity Insights

CW Talent Solutions; Greater London; On-site; GBP 80,000 - 140,000; Full time

Benefits offered by this job

Competitive compensation

Performance-based upside

World-class resources and teams

Job summary

CW Talent Solutions partners with a world-renowned investment firm to recruit elite Quantitative Researchers for the Equities Fundamental Research team. You will work closely with Portfolio Managers and Analysts to generate investment insights through rigorous quantitative methods.

Role focuses on building models, extracting signals from large datasets, and applying ML techniques to enhance stock selection, risk management, and equity strategies. London-based, on-site with the team.

Qualifications

• 2+ years' experience in quantitative research or data science.
• Strong knowledge of fundamental investing and equity markets.
• Proficiency in Python and data science libraries.
• Experience working with large, diverse datasets (e.g., financial statements, alternative data).
• Exceptional problem-solving and communication skills.

Responsibilities

• Partner with Fundamental Analysts to enhance investment processes using data and quantitative research
• Build predictive models to support stock selection, portfolio construction, and risk management
• Develop tools and frameworks for fundamental signal extraction
• Apply alternative data analysis and machine learning techniques to derive actionable insights
• Collaborate across global teams to drive innovation in equity investing

Skills

Quantitative research

Python

Data science libraries

Equities / equity markets

Large datasets

Job description

CW Talent Solutions partners with a world-renowned investment firm to recruit elite Quantitative Researchers for the Equities Fundamental Research team. You will work closely with Portfolio Managers and Analysts to generate investment insights through rigorous quantitative methods.

Role focuses on building models, extracting signals from large datasets, and applying ML techniques to enhance stock selection, risk management, and equity strategies. London-based, on-site with the team.