About the Role:
We are seeking a highly skilled Senior Quantitative Developer to drive the development of our advanced machine learning platforms, combining expertise in quantitative development, data engineering, and machine learning. In this role, you will be instrumental in building and optimizing ML infrastructure to support model development, backtesting, and deployment for quantitative strategies. You will work closely with quants, data scientists, and engineering teams to deliver scalable, high-performance solutions that support algorithmic trading and financial analytics.
Responsibilities:
- Platform Development: Design, build, and maintain the infrastructure and tools that enable large-scale ML model development, testing, and deployment for quantitative strategies.
- Quantitative Model Integration: Collaborate with quant researchers and data scientists to implement, optimize, and deploy machine learning models within the quant research environment.
- Data Pipeline Engineering: Develop and manage data pipelines for high-frequency and low-latency financial data, enabling efficient data retrieval, transformation, and storage.
- Performance Optimization: Fine-tune ML model and platform performance to optimize resource usage, processing speed, and model accuracy in a production environment.
- Algorithmic Backtesting: Build and maintain robust backtesting frameworks, supporting the research and validation of models across various trading strategies and asset classes.
- Risk Management Support: Implement tools and processes to assess and monitor model risk, ensuring compliance with regulatory requirements and internal standards.
- Documentation and Best Practices: Document platform architecture, model development workflows, and operational processes; promote software engineering best practices, including testing, version control, and code review.
- Mentorship and Collaboration: Provide technical guidance to junior developers and collaborate across teams to share insights, drive innovation, and achieve strategic objectives.
Requirements:
- Experience: 5+ years in quantitative development, financial software engineering, or similar, with a strong focus on ML infrastructure or platforms.
- Programming Skills: Expertise in Python, C++, or Java; strong knowledge of ML libraries (TensorFlow, PyTorch, Scikit-Learn) and quantitative libraries (NumPy, Pandas).
- Financial Domain Knowledge: Solid understanding of financial markets, asset classes, and quantitative finance principles, including statistical analysis, time series modeling, and risk management.
- High-Performance Computing: Experience with distributed systems, high-performance computing, and parallel processing, ideally for large-scale data processing or model training.
- Data Engineering: Proficiency in working with SQL and NoSQL databases, ETL pipelines, and big data technologies (e.g., Spark, Hadoop) to support data-driven ML applications.
- MLOps and Cloud: Hands-on experience with MLOps tools (e.g., Docker, Kubernetes, Airflow) and cloud platforms (AWS, GCP, or Azure) for model deployment and monitoring.
- Analytical and Problem-Solving Skills: Strong analytical thinking, with a proactive approach to complex problem-solving in a fast-paced environment.
- Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, Financial Engineering, or a related field; CFA, CQF, or similar certifications are a plus.
