Senior Quantitative Developer (Crypto Trading Strategies)
Location: Fully Remote (Global)
Employment: Full-Time or Part-Time
Compensation: Competitive hourly rate + performance-based profit sharing (up to ~30%)
Language: English (B2+ required)
About the Role
We are supporting a high-performing, remote-first trading team operating in centralized crypto markets (CEX). This role is ideal for a senior quantitative developer who enjoys owning strategies end-to-end and working close to live trading and execution.
You will combine statistical methods, machine learning, and market microstructure knowledge to build and deploy alpha-generating strategies that perform across varying market regimes.
What You’ll Be Working On
- Designing and implementing statistical arbitrage strategies (mean reversion, cointegration, funding-rate arbitrage)
- Building quantitative and time-series models using techniques such as Kalman Filters, PCA, and machine learning
- Developing momentum-based strategies using technical indicators and volume analysis
- Creating and optimizing low-latency signal generation and execution systems
- Running back tests, risk models, and performance optimization on live and historical data
What We’re Looking For
- 5+ years’ experience developing quantitative trading strategies in production environments (e.g. hedge funds, prop trading, fintech, or crypto-native firms)
- A demonstrable track record of profitable strategies deployed in live markets
- Strong understanding of market microstructure, particularly in digital assets and futures
- Master’s or PhD in Computer Science, Mathematics, Physics, Statistics, or a related quantitative field
Technical Skills
Programming
- Expert-level Python (NumPy, Pandas, scikit-learn, PyTorch / TensorFlow)
- Strong Rust experience for performance-critical components
- Modern engineering practices: Git, testing, CI/CD
- Containerized and cloud-native environments (Docker, Kubernetes, AWS or GCP)
Quantitative & ML Methods
- Kalman Filters, PCA, ARIMA, GARCH, regime-switching models
- Factor models, Hidden Markov Models
- Options pricing (e.g. Black-Scholes, Monte Carlo)
- Deep learning approaches (LSTMs, Transformers, reinforcement learning)
Infrastructure
- Low-latency systems and real-time data pipelines
- Exchange API integration (REST, WebSocket, FIX)
- High-performance databases (PostgreSQL, TimescaleDB, ClickHouse)
Nice to Have
- Direct experience with cryptocurrency trading infrastructure
- Published research in quantitative finance or machine learning
- Open-source contributions or work with alternative data sources
What’s On Offer
- Competitive compensation with meaningful performance-based upside
- High level of autonomy and ownership over research and implementation
- Access to proprietary data and strong computational resources
- 100% remote, with flexible working arrangements to suit your lifestyle