Crypto Quantitative Engineer - Pricing & Risk Tools Hybrid

Aplo

Paris

Hybride

EUR 55 000 - 62 000

Plein temps

14 jours+
Générateur de candidature

Une candidature conçue pour ce poste — un CV et une lettre de motivation personnalisés qui correspondent à l’offre.

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Avantages offerts par ce poste

Health insurance
Lunch vouchers €12.20 per day
Hybrid work model
Public transport reimbursed (Navigo)
Monthly sports subscription €45
Annual culture allowance €200
Annual training budget €1000

Résumé du poste

Aplo is hiring a Quantitative Developer to join a hands-on, code-first team in Paris. You will design, implement, and maintain pricing, trading, and risk tools, collaborating with quants and IT while reporting to the CIO.

We seek solid CS and math skills, fluency in Python with NumPy/Pandas/SciPy, and a pragmatic, well-tested approach. Growth toward greater technical ownership is expected in a hybrid environment.

Qualifications

  • Solid CS and math background with strong Python and NumPy, Pandas, SciPy.
  • Experience with software development practices and risk management basics.
  • Fluent in French and English (C2 in both).

Responsabilités

  • Design, develop, and maintain pricing and risk tools.
  • Automate calculations and reporting for trading and risk teams.
  • Integrate quantitative models into production systems.
  • Test, validate, and document developed tools.
  • Collaborate with quants and IT to meet business needs.
  • Maintain and improve existing systems and infrastructure.

Connaissances

Python
C++
Java
Git
CI/CD
Data pipelines
Financial markets basics

Formation

Master's degree in Computer Science, Financial Engineering, Applied Mathematics

Outils

NumPy
Pandas
SciPy
CI/CD tooling

Description du poste

Aplo is hiring a Quantitative Developer to join a hands-on, code-first team in Paris. You will design, implement, and maintain pricing, trading, and risk tools, collaborating with quants and IT while reporting to the CIO.

We seek solid CS and math skills, fluency in Python with NumPy/Pandas/SciPy, and a pragmatic, well-tested approach. Growth toward greater technical ownership is expected in a hybrid environment.

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