We are looking for a Senior / Quantitative Developer to strengthen our research and execution systems. This role bridges systematic research, production-level Python coding, and data engineering in capital markets.
- You will collaborate with strategists, alpha researchers, and portfolio managers, as well as data engineers and platform operators.
- Your main goal will be to transform research ideas into reliable services and tools for our portfolios, strategies, reports, and command-line workflows.
- Initially, the focus will be on building and integrating systems rather than creating new research models. You'll primarily work on developing a new portfolio management toolkit and reporting platform
Responsibilities:
- Design and Maintain Systems: Build and manage models, services, and command-line workflows for portfolios and strategies.
- Turn Ideas into Code: Transform investment hypotheses into reliable Python components with clear typing and observability.
- Optimize Data Processes: Collaborate with teams to improve the ingestion and storage of large market datasets (PostgreSQL, S3, Parquet).
- Build Reusable Tools: Create quantitative research utilities that evolve from notebooks into libraries, following engineering standards.
- Test Analytics Pipelines: Set up and validate performance and attribution pipelines using pytest and targeted tests.
- Participate in Reviews: Engage in design, code, and release planning to ensure predictable and traceable deployments.
- Take Ownership: Identify gaps, improve processes, and drive initiatives without waiting for direction.
Required Skills
- Advanced Python Proficiency: Strong skills in Python, including typing, packaging, and tools like Ruff, Black, and pytest.
- Quantitative Experience: Background in statistics, optimization, or signal processing with model deployment experience.
- SQL Fluency: Proficient in SQL (PostgreSQL) and comfortable with Alembic migrations and query templates.
- Version Control Knowledge: Experience with git workflows and command-line tools.
- Data Handling Skills: Understanding of precision issues, data hygiene, and safety in financial pipelines.
- Self-Directed Learner: Ability to manage tasks independently and engage stakeholders as needed.
- AWS Experience: Familiar with AWS services (EC2, S3, IAM) and modern data infrastructure.
- Containerization Familiarity: Knowledge of Docker and CI systems for code quality.
- Job Orchestration: Experience managing analytics jobs across notebooks and scripts.
Nice to Have:
- Portfolio Knowledge: Familiarity with portfolio construction and risk modeling.
- Data Integration Experience: Exposure to third-party data providers and API/CLI adapters.
- Distributed Computing Understanding: Basic knowledge of tools like Spark or Ray.
Preferred:
- Systematic Investing Background: Previous work in asset management or trading systems.
- Data Domain Knowledge: Understanding of provider resolution and rebalancing.
- Documentation Skills: Ability to write clear technical documents.
