Quantitative Execution Strategist

WorldQuant LLC; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A financial technology firm in Singapore is seeking an Execution Researcher to enhance trading strategies using Python. The role involves developing execution algorithms, analyzing transaction costs, and improving model accuracy with machine learning. Ideal candidates will have a PhD or Master's in a quantitative field and experience in data analysis. Join a collaborative team aiming to optimize financial strategies and drive innovation in trading.

Qualifications

• 0–4 years' experience in quantitative research and/or development for systematic or execution strategies.
• Solid Python programming skills are essential.
• Experience manipulating large-scale datasets.

Responsibilities

• Develop specifications for new execution algorithms.
• Implement robust trading strategies and tools using Python.
• Conduct in-depth Transaction Cost Analysis to optimize trading.

Skills

Python programming

Data structures and algorithms

Regression models

Time-series analysis

Applied machine learning

Education

PhD/Master's in a quantitative field

Tools

SQL

C/C++

Job description

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.

WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.

Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.

The Role

The Quantitative Execution team is seeking an Execution Researcher to further refine WorldQuant's execution capabilities across a variety of cash and derivative products to be based in Singapore.

Key Responsibilities

• Algorithm Design & Implementation
  • Develop detailed specifications for new and enhanced execution algorithms.
  • Implement, test, and deploy robust quantitative trading strategies and tools, primarily using Python.
• Tooling
  • Build robust and reusable tools in Python to improve the overall team productivity.
  • Research, develop, and refine sophisticated market impact models to seek to predict and minimize the cost of trading.
  • Leverage large datasets and advanced statistical/machine learning techniques to improve model accuracy.
• Transaction Cost Analysis (TCA)
  • Conduct in-depth Transaction Cost Analysis (TCA) to evaluate the performance of execution strategies, identify drivers of transaction costs, and pinpoint opportunities for optimization.
  • Provide actionable insights to portfolio managers and traders.
  • Work closely with the QES Lead for APAC to align strategies with regional market nuances and business objectives.
  • Collaborate with trading desks, technology teams, and other quantitative researchers to integrate solutions and drive innovation.

What You'll Bring

• PhD/Master's in a quantitative field (CS, Mathematics, Statistics, Physics, Engineering, Quant Finance) from a top university, with 0–4 years' experience in quantitative research and/or development for systematic or execution strategies.
• Solid Python programming skills (required), strong understanding of data structures and algorithms, and experience manipulating large-scale datasets using SQL; C/C++ is a strong differentiator.
• Strong expertise in regression models, time-series analysis, and applied machine learning, with exposure to deep learning and/or reinforcement learning, ideally in data-intensive, production-oriented environments.