Tiger Recruitment is partnering with a highly successful Tier 1 multi - strategy Hedge fund seeking a talented Quant Researcher to drive the full life cycle of their trading strategies and build out a "greenfield" systematic equity capability.

We aren't looking for someone to just maintain legacy systems; we are seeking an entrepreneurial builder. In this role, you won't just be a cog in a machine, you will be viewed as the true owner and expert of the work you produce.

The Quant Researcher (Portfolio Construction & Signal Research) will drive the full life cycle of their trading strategies.

What You’ll Do For Them

• Portfolio Construction Research: You will take ownership of all aspects of their portfolio construction and optimization process. This includes:
• Designing optimization problems, objective functions, and constraints to efficiently hedge risk and capture alpha.
• Improving their existing methods for optimally combining trading signals.
• Developing novel methods to efficiently turn raw signals into implementable portfolios.
• Developing algorithms to dynamically adjust position sizing, risk tolerance, and signal weighting as the market opportunity set shifts.
• End-to-End Signal Research: You will develop new systematic equity signals and improve existing ones using market data, alternative data, and their proprietary internal datasets. You will be responsible for the full life cycle, from initial idea inception to production deployment, live monitoring, and post-trade analysis.
• Data Engineering: You will onboard new datasets and build robust, scalable ETL pipelines to ingest, clean, and normalize large-scale structured and unstructured data for alpha and portfolio research.

What My Customer is Looking For

• Experience: 3–6 years of quantitative research experience within a hedge fund, bank, or proprietary trading firm.
• Education: A Bachelor’s or Master’s degree in a highly quantitative field (Mathematics, Statistics, Computer Science, Physics, or Engineering).
• Market Knowledge: A deep understanding of portfolio optimization, equity factor risk models, and market microstructure. Ideally, you also bring some familiarity with the fundamental long/short investment process.
• Mathematical Expertise: Demonstrated success using statistics, linear algebra, numerical optimization, and modern machine learning techniques to solve messy, real-world data problems.
• Technical Stack: Exceptional proficiency in Python and core data science/ML libraries (specifically polars, pandas, numpy, scipy, sklearn, and tensorflow/pytorch), alongside strong SQL skills for handling massive financial datasets.
• Engineering & DevOps: A proven ability to write robust, production-level code, combined with familiarity with modern DevOps and orchestration tools (such as Docker, ControlM, or Airflow).
• Soft Skills: A disciplined approach to research and planning, with the ability to clearly communicate complex technical results to senior technical and business stakeholders.

What's on Offer

This is a rare opportunity to join an elite investment team where technology and research are central to competitive advantage. The successful candidate will work alongside some of the industry's brightest minds, contribute directly to investment outcomes, and help build the next generation of data-driven research and trading infrastructure.

Job Ref: NM 188201