Senior Quantitative Developer
at Millennium
Posted 21 days ago
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Seeking a Senior Quantitative Developer to design and build a next-generation alpha research pipeline covering data ingestion, model evaluation, and reporting. The role involves developing and maintaining production-quality Python code, implementing testing and CI best practices, and tuning runtime performance of data applications. The candidate will provide technical leadership, mentor junior team members, and collaborate closely with quantitative researchers and stakeholders to prioritize and deliver features.
The Central Execution Book (CEB) is a global effort to optimize the firm’s execution across business lines and asset classes. At a high level the CEB seeks to improve execution quality by reducing market impact and controlling information leakage, but the group’s mandate also includes the deployment of the firm’s risk capital to do so. The above requires solving complex technical and quantitative problems.
We are looking for a highly driven, results-oriented Senior Quantitative Developer to join a dynamic group tasked with developing our next-generation alpha research pipeline, encompassing data ingestion to model evaluation and reporting. We are at the forefront of industry-leading initiatives applying technology, quantitative analysis, and data-driven methodologies to our execution process.
Principal Responsibilities
The successful candidate will be expected to:
- Help design and contribute to the alpha research platform
- Support, maintain, and test their own code following best practices, including unit testing, regression testing, documentation, and automation within typical CI processes
- Provide leadership and vision to help determine the overall direction, design, and architecture of the alpha research pipeline
- Mentor junior resources
- Regularly interact with quantitative researchers and other stakeholders, and prioritize and implement features
Qualifications / Skills Required
- 5+ years of Python experience in a quantitative finance setting
- Familiarity with linear models and basic statistics for creating model evaluation and reporting workflows
- Familiarity with the Python data science ecosystem, including dashboarding and popular ML libraries such as Plotly, Altair, JAX, TensorFlow, and PyTorch
- Prior experience building alpha research or machine learning pipelines
- Highly analytical with strong problem-solving skills and attention to detail
- Strong communication skills, with the ability to explain technical and sophisticated concepts clearly and concisely
- Ability to tune and debug runtime performance of data applications
- Familiarity with C++/Rust/CUDA to debug and profile underlying native code in ML libraries (Nice to have)
