Position: Quantitative Developer
Company Overview:
We are a dynamic trading and research team, focused on developing advanced quantitative strategies within the cryptocurrency space. Our work involves leveraging deep technical analysis, machine learning, and innovative tools to create profitable trading systems in an evolving market environment.

Role Overview:
We are looking for a Quantitative Developer to design and build robust, scalable solutions for trading strategy development and execution. The ideal candidate will have a strong background in Python programming, experience with financial data, and a track record of developing infrastructure for backtesting, real-time analytics, and trading systems.

Responsibilities:
• Design and implement scalable trading platforms for algorithmic strategy development and execution.
• Develop backtesting frameworks to evaluate strategies across various market conditions.
• Build and maintain data pipelines for large-scale financial datasets.
• Collaborate with analysts and researchers to integrate trading signals and indicators into the platform.
• Ensure platform scalability, reliability, and performance for real-time trading.
• Monitor and optimize platform performance to maintain operational efficiency.

Qualifications:
• Bachelor’s degree in Computer Science, Engineering, or a related technical field.
• 2+ years of experience in Python development for quantitative research or trading systems.
• Experience with financial libraries and tools such as Pandas, NumPy, and backtesting frameworks.
• Experience in designing and optimizing database systems (SQL/NoSQL).
• Familiarity with cloud computing platforms (AWS, GCP, or Azure).
• Understanding of financial markets and trading concepts (e.g., order types, risk management).
• Strong problem-solving skills and ability to work in a fast-paced environment.

Preferred Skills:
• Experience with real-time data streaming and API development.
• Knowledge of cryptocurrency or traditional financial markets.
• Understanding of quantitative trading strategies, including range-bound and trend-following.
• Familiarity with machine learning or statistical modeling in a financial context.