We are looking for a versatile Quantitative Software Engineer to join our Data Innovation Team. In this role, you won't just be managing data; you will be building the mathematical logic that serves as the brain of our organization. You will own the lifecycle of complex business solutionsarchitecting the underlying mathematics, implementing the logic in the backend, and ensuring these models secure the company's position through precise probability and risk calculations.
We value Builders who thrive at the intersection of advanced mathematics and system engineering. You should be able to utilise AI-assisted development tools to rapidly translate abstract mathematical theories into production-ready backend code.
Key Responsibilities
- Solution Architecture: Translate high-level business requirements into technical mathematical specifications.
- Mathematical Backend Development: Build and maintain full-stack applications, ensuring the core calculation logic is seamlessly integrated into the backend.
- Risk & Probability Engineering: Design and implement rigorous mathematical models to calculate probabilities and secure the company's position against financial risk.
- Data & AI Pipelines: Build and optimize declarative pipelines and orchestrate complex workflows to serve mathematical and Machine Learning models.
- AI-Augmented Coding: Leverage modern AI coding tools to accelerate the development of complex algorithms and backend services.
- Operational Excellence: Manage the full application lifecycle, including governance via Unity Catalog and model tracking via MLflow.
Technical Requirements
The Essentials:
- Education: Degree in Mathematics, Statistics, Computer Science, or Software Engineering.
- Software Engineering: Deep mastery of Python (PySpark), Java, or C++.
- Mathematical Proficiency: Expertise in probability theory, statistical modeling, and calculating complex risk distributions.
- Data-Oriented Design: You should understand mathematical logic and data structures as well as you understand OOP.
- Full-Stack Mindset: Comfort moving from backend mathematical logic to frontend API consumption.
- Vibe Coding Mastery: High proficiency in using AI-assisted development tools to speed up coding tasks and solve algorithmic challenges.
The Lakehouse Stack (Desired):
- Databricks Ecosystem: Hands-on experience with Delta Lake, Spark SQL, and Databricks Workflows.
- AI Operations: Experience using MLflow to deploy and monitor models in production.
- DevOps for Data: Familiarity with Databricks Asset Bundles (DABs), Git integration, and containerization (Docker)
