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Senior Quant Developer (Python)

McGregor Boyall
Dubai, UAE
Full-time
Mid-Senior
Onsite
Discovered 3 weeks ago
PythonQuantitative developmentQuantitative research platformsTrading strategy productionizationLarge-scale data preparation and cleansingSoftware engineering standards

Full Job Posting

Role Overview

A global investment manager is hiring a Senior Quantitative Developer in Dubai.

Join a global team of cross-asset Quant Developers and Researchers shaping a research platform.

The role focuses on Python, agentic coding tools, large-scale data workflows, and a cross-asset research platform.

Relocation and visas are covered.

Responsibilities

  • Help quantitative researchers bring trading strategies into production.
  • Provide technical leadership and raise engineering standards across the team.
  • Improve software engineering practices across research workflows, including effective use of agentic coding tools.
  • Automate and streamline large-scale data preparation and cleansing.
  • Build, enhance, and support quantitative research tools.
  • Evolve the platform as the business changes.

Described Career Trajectory

In the first year, learn the platform, earn researchers’ trust, address longstanding issues, and become a first contact when data looks wrong.

In the second year, lead engineering standards, review culture, testing practices, and how the team uses agentic coding tools.

By the third year, take strategies from research into production across several asset classes and develop the option to run a research platform team or move closer to research.

Requirements

  • Experience developing quantitative software in Python.
  • Ability to partner with quantitative researchers to bring trading strategies into production.
  • Ability to provide technical leadership and improve engineering standards across a team.
  • Experience improving software engineering practices across research workflows, including effective adoption of agentic coding tools.
  • Ability to automate and streamline large-scale data preparation and cleansing.

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