Quantitative Researcher

Orchid AI; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Competitive compensation

Career growth opportunities

Access to cutting-edge AI tools

Job summary

Orchid AI is looking for a Quantitative Researcher to join their investment research team. This role will involve conducting quantitative research, designing systematic investment strategies, and collaborating with portfolio managers and AI engineers.

The ideal candidate should have a strong background in quantitative analysis, programming skills in Python, and a keen interest in financial markets and AI technologies. Enjoy flexible or remote work arrangements while contributing to innovative investment strategies.

Qualifications

• 3+ years of experience in quantitative research or asset management.
• Experience with large structured and unstructured datasets.
• Strong communication skills to explain quantitative concepts.

Responsibilities

• Conduct quantitative research to develop alpha-generating signals.
• Design and refine systematic investment strategies.
• Monitor live strategies and improve models based on market conditions.

Skills

Quantitative analysis

Programming in Python

Machine learning

Statistical modeling

Data analysis

Education

Bachelor's, Master's, or PhD in relevant field

Tools

Python libraries for data science

Cloud computing environments

Job description

Orchid AI is building an AI-native asset management platform that combines quantitative research, machine learning, agentic AI systems, and alternative data to generate differentiated investment insights and systematic investment strategies. Our mission is to create a modern research and portfolio management stack where human expertise and AI work together to discover, validate, and scale alpha opportunities across global markets.

Role Overview

We are seeking a highly motivated Quantitative Researcher to join our investment research team. This role offers the opportunity to work across the full investment research lifecycle, from idea generation and data acquisition through signal development, portfolio construction, backtesting, and live strategy monitoring.

You will work closely with portfolio managers, AI engineers, and data scientists to develop systematic investment strategies powered by alternative data, machine learning, and agentic AI workflows. The ideal candidate combines strong quantitative skills with a deep curiosity about financial markets and emerging AI technologies.

Location

Flexible / Remote (with preference for overlap with U.S. and European market hours)

Key Responsibilities

• Conduct quantitative research to identify and develop alpha-generating investment signals across equities and other liquid asset classes.
• Design, test, and refine systematic investment strategies using statistical, machine learning, and AI-driven approaches.
• Source, evaluate, and integrate alternative, fundamental, macroeconomic, and market data into research pipelines.
• Develop robust back testing frameworks and research infrastructure to evaluate strategy performance and risk characteristics.
• Apply modern AI and agentic systems to automate research workflows, data analysis, idea generation, and portfolio monitoring.
• Collaborate with portfolio managers to translate research insights into investable strategies and portfolio construction frameworks.
• Build predictive models for forecasting returns, risk, market regimes, and factor behavior across varying time horizons.
• Monitor live strategies and continuously improve models based on changing market conditions and new data sources.
• Contribute to the development of Orchid AI’s proprietary investment intelligence platform and research ecosystem.
• Communicate research findings clearly through written reports, presentations, and collaborative discussions.

Required Qualifications

• Bachelor's, Master's, or PhD in Mathematics, Statistics, Computer Science, Physics, Engineering, Economics, Finance, or a related quantitative discipline.
• Strong programming skills in Python and experience with quantitative research libraries and data science tools.
• Strong foundation in statistics, probability, optimization, machine learning, and financial modeling.
• Experience working with large structured and unstructured datasets.
• Ability to independently formulate hypotheses, design experiments, and evaluate results rigorously.
• Strong communication skills and the ability to explain complex quantitative concepts to both technical and investment audiences.

Preferred Experience

• 3+ years of experience in quantitative research, systematic investing, hedge funds, asset management, proprietary trading, or fintech.
• Experience with alternative datasets, including corporate, consumer, geospatial, web, transaction, or other non-traditional data sources.
• Familiarity with cloud computing environments and modern data engineering practices.
• Experience applying machine learning, large language models (LLMs), or agentic AI systems to investment research and decision-making.

Highly Valued Experience

• Building proprietary datasets or investment intelligence products.
• Research involving cross-sectional equity selection, statistical arbitrage, factor investing, or multi-asset strategies.
• Experience integrating heterogeneous data sources into unified predictive frameworks.
• Developing AI agents for research automation, knowledge extraction, market intelligence, or portfolio management workflows.
• Working in entrepreneurial, fast-moving investment or technology environments.
• Experience taking research from concept to live deployment and ongoing performance management.

Why Join Orchid AI

• Build at the intersection of AI, quantitative finance, and asset management.
• Work directly with founders and senior investment professionals.
• Access cutting-edge AI tools, alternative datasets, and research infrastructure.
• Significant opportunity for ownership, impact, and career growth.
• Competitive compensation, performance incentives, and equity participation for exceptional candidates.