Quantitative Researcher

Durlston Partners LLP; Greater London; On-site; GBP 136,000 - 212,000; Full time

Job summary

Durlston Partners LLP in London or New York is seeking a Quantitative Researcher to develop and run mid-frequency stat arb signals across global equities. You will transform raw data into production-ready models and contribute to portfolio construction and risk analysis.

You’ll work with price, fundamental and alternative data, backtest rigorously, and take strategies from research into live trading while monitoring and improving them.

Qualifications

• 3+ years researching systematic equities strategies that have traded live.
• Strong Python. C++ or kdb+ is a plus.
• Solid grounding in statistics, time series analysis and machine learning.
• Rigorous approach to backtesting and avoiding overfitting.
• Degree in mathematics, statistics, physics, computer science, engineering or a related quantitative field. A PhD is common but not essential

Responsibilities

• Research and develop systematic alpha signals across global equity universes.
• Work with price, fundamental and alternative data, from raw data through to production-ready signals.
• Contribute to portfolio construction, risk modelling and transaction cost analysis.
• Take strategies from research into live trading, then monitor and improve them

Skills

Python

Statistics

Time Series

Machine Learning

Education

Degree in mathematics/ statistics/ CS/ engineering

Tools

C++

kdb+

Job description

Job title: Quantitative Researcher - Equities Statistical Arbitrage

Location: London or New York (also open in Chicago, Hong Kong, Singapore, Dubai and Paris)

I’m working with a leading global systematic hedge fund that is growing its equities research effort. They have headcount across several stat arb teams and are hiring Quantitative Researchers to build and run mid-frequency strategies, with holding periods from intraday to a few weeks.

You’ll sit in an established team with significant capital behind it, plus the data, compute and execution infrastructure of a top multi-manager platform.

The role

• Research and develop systematic alpha signals across global equity universes
• Work with price, fundamental and alternative data, from raw data through to production-ready signals
• Contribute to portfolio construction, risk modelling and transaction cost analysis
• Take strategies from research into live trading, then monitor and improve them

Requirements

• 3+ years researching systematic equities strategies that have traded live, at a hedge fund, prop trading firm, bank QIS desk or quant asset manager
• Strong Python. C++ or kdb+ is a plus
• Solid grounding in statistics, time series analysis and machine learning
• Rigorous approach to backtesting and avoiding overfitting
• Degree in mathematics, statistics, physics, computer science, engineering or a related quantitative field. A PhD is common but not essential

Nice to have

• Experience with alternative datasets
• Exposure to intraday signals or execution research
• Ability to discuss past strategy performance (Sharpe ratio, capacity, turnover)

What’s on offer

• Highly competitive base plus a bonus directly linked to performance
• Flexibility on location across the firm’s global offices
• A collaborative research environment with a long track record in systematic equities