Equities Quant Researcher & Developer - ML & Tools

Goldman Lloyds International; New York (NY); On-site; USD 150,000 - 250,000; Full time

Job summary

Goldman Lloyds International seeks a PhD-level Quantitative Researcher/Developer to join its equities research pod. The role blends quantitative research, statistical modelling and Python development to support live investment decisions alongside the Portfolio Manager.

You will develop Python-based research and backtesting frameworks, analyze vast datasets, and apply ML/time-series methods to generate actionable insights for stock selection and risk management.

Qualifications

• PhD in Mathematics, Statistics, Physics, Computer Science, Engineering or a related quantitative discipline.
• Strong Python programming and quantitative research skills.
• Deep understanding of statistics, probability, optimization and/or machine learning.
• Experience developing analytical models and working with complex datasets.
• Equity research, systematic investing or financial markets exposure advantageous.
• Strong interest in applying quantitative methods directly to investment decisions.

Responsibilities

• Research quantitative signals and models across Equities and Long/Short investment strategies.
• Develop Python-based research, modelling and backtesting frameworks.
• Analyze large-scale market, fundamental and alternative datasets.
• Apply statistics, machine learning and time-series analysis to investment research.
• Build tools supporting stock selection, portfolio construction, factor analysis and risk.
• Work directly with the Portfolio Manager to translate research into actionable investment insights.

Skills

Quantitative research

Python development

Statistics & ML

Time-series analysis

Data analysis

Education

PhD in quantitative field

Tools

Python

Backtesting frameworks

Job description

Goldman Lloyds International seeks a PhD-level Quantitative Researcher/Developer to join its equities research pod. The role blends quantitative research, statistical modelling and Python development to support live investment decisions alongside the Portfolio Manager.

You will develop Python-based research and backtesting frameworks, analyze vast datasets, and apply ML/time-series methods to generate actionable insights for stock selection and risk management.