Quant Developer

Millennium is a global hedge fund with more than 4,000 employees and offices in the United States, Europe and Asia. We were founded in 1989, and we employ a global multi-strategy investment approach, opportunistically engaging in a broad array of trading and investing strategies. Millennium has differentiated itself from other investment management firms through our consistent ability over the last 25 years to generate returns that have not been correlated to the general market.

The talent and dedication of our people are critical to our success. We offer an opportunity for developing one’s professional career while working with individuals trained in a variety of disciplines in a collegial and dynamic environment. We also offer a broad range of competitive benefits on a global basis.

1. Principal Responsibilities 

• Develop frameworks to create volatility models for systematic equity index options pricing data, and support the technical aspects of development, including connectivity to our Firm’s internal trading, risk, and compliance platforms.
• Implement libraries in C++ or Python shared objects.
• Code in Python powered by generative artificial intelligence (AI) applications.
• Generate parameters for implied volatility forecasts leveraging machine learning techniques.
• Analyze large datasets to validate modeling results, and prepare historical options data sets for model calibration, generating parameter time-series data for trades.
• Develop databases of parameter history over frequent calibrations for training alpha signal analysis.
• Build, automate, and test prediction models in financial markets using methods, such as neural networks and LLMs.
• Work with the Senior Portfolio Manager to quickly iterate and optimize models actively in production.

 2. Qualifications/Skills Required

• Position requires the completion of a Master’s degree program in Computer Science, Information Technology, Digital Solutions or a closely related technology-focused field.
• 1 year of work experience and/or academic training (internships, apprenticeships, etc.) must include:
• Expertise in statistical learning and quantitative finance techniques, including Bayesian inference, time series analysis and stochastic processes, to explore, analyze, and harness a large variety of datasets to develop strong predictive models.
• Utilization of Apache Airflow workflow management platform for data engineering pipelines.
• Utilization of software engineering technology, including Linux or Unix, Docker, and Kubernetes, and most importantly, modern technology, including Grafana data visualization platform, and machine and deep learning techniques.
• Coding in Python powered by genAI applications.
• Innovative utilization of genAI and LLMs to deliver quantitative technical solutions.
• Eagerness to adopt new AI and ML techniques that will further drive technological transformation.
• Agile or DevOps methodology for rapid software development.
• Strong understanding of systematic trading platforms and equity financial data is required.