intropic - Quantitative Developer
Requirements
• STEM graduate (or final-year student) with demonstrable coding ability. • Strong Python skills (other OOP languages such as Java or C++ are welcome and seen as a plus). • Practical experience with SQL and relational databases • Comfortable with the command line and modern version-control workflows (example: GitHub / GitLab / Bitbucket). • Strong communicator, able to explain technical work to both technical and non-technical audiences. • Independent, self-driven learner who takes ownership and can work across disciplines. • Familiarity with automated testing and general software engineering best practices (code review, CI concepts). • 0–2 years professional experience in a software engineering, quantitative developer, or data engineering role. Experience within the finance industry is a strong plus. • Good working knowledge of NumPy and Pandas. • Familiarity with backend development and async programming in Python / modern Python frameworks. • Experience with containerisation and cloud deployments (Docker, cloud platforms such as AWS). • Practical exposure to financial data via university projects, internships or full-time work.
Responsibilities
• Collaborate closely with product managers, research analysts and other engineers to define project scope, translate research into product requirements, and deliver concrete technical solutions. • Maintain, extend and improve Intropic’s suite of financial-data products, from backend data services to client-facing features. • Design, implement and ship clean, well-tested, production-ready Python code and reusable Python libraries used across the stack. • Build and maintain data processing pipelines that ingest, transform and validate large and heterogeneous financial datasets. • Build production REST APIs and data services, and use SQL to analyse large relational datasets. • Deploy production-quality code to cloud infrastructure (cloud providers, CI/CD pipelines) and own the end-to-end release process. • Work with analysts to operationalise quantitative research: production-wise models, automate experiments, and ensure reproducible results.
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