Quantitative Trading Analyst

About The Company We are a fast-growing startup at the intersection of quantum computing and financial services, applying cutting-edge technology and innovative solutions to solve complex business problems for our clients.

Our mission is to deliver real-world impact through high-quality product development, operational excellence, and strategic client engagement. We value collaboration, curiosity, and ownership, and are building a team that thrives in a fast-paced, high-growth environment.

About The Role We run algorithmic trading strategies across FX, equities, commodities and crypto, built on quantum and quantum-inspired research. Our scientists generate signals and our engineers run the platform. You connect the path between them, from a promising idea, through backtest and validation, to trading.

You would own that path. Partly by building the machinery: the backtest harness, the validation gates, the route to deployment, so the research team ships without waiting on anyone. Partly by doing the work yourself: applying machine learning to strategy development where it earns its place, and being honest about where it does not.

Key Responsibilities Own the path from research idea to live strategy, and make it faster and more repeatable.

Build and maintain the backtest, validation and deployment tooling the research team depends on.

Apply machine learning to strategy development where it outperforms the classical approach, and say so plainly when it does not.

Guard against look-ahead bias, overfitting, survivorship, and build those guards into the tooling.

Work with the science team on turning their research into something deployable, and with engineering on making it run.

Document and hand over, so the machinery outlives you.

Required Experience Three or more years applying quantitative or machine learning methods to financial market data.

Strong, production-quality Python that other people can maintain.

Direct experience taking a model or strategy from research into something running live.

A clear understanding of how backtests mislead, and how to design against it.

Comfort working across teams without formal authority over either.

Ideal Experience Built or maintained a backtesting or research platform that others relied on.

Familiarity with FX, futures or crypto market structure.

C++ or Rust alongside Python.

CI/CD, automated testing and infrastructure-as-code habits.

Early-stage or high-growth experience, where the tooling did not already exist.

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