The Athena Quant Engineering team provides a Jupyter-based platform through which 1000+ users access Athena Python to perform research, data science, and machine learning. Athena Python includes many open-source libraries alongside internally developed ones. In addition to performing research and analysis in JupyterLab, our users can deploy their notebook-based work into production using other components of the Jupyter ecosystem to generate reports and dashboards.
We are looking for a Python/Jupyter developer who wants to support and enhance notebook-based workflows from the research and innovation stages right through to production. While we are looking for experienced professionals, we are happy to consider confident engineering professionals with alternative skills, who would be willing to work with our core tech stack: Python, React, JupyterLab.
Skills
The ideal candidate would have some of the following skills, coupled with a strong desire to learn others:
Python and modern JavaScript development.
Professional software engineering practices and understanding of the full software development life cycle (including coding standards, code reviews, source control management, build processes, testing, and operations).
Experience scaling a system from proof of concept to production readiness.
JupyterLab customization or deployment.
Experience with Kubernetes and developing public cloud applications.
General knowledge of Python for analytics (e.g. NumPy, Pandas, SciPy, etc).
Experience of building tools for data scientists.
Notebook usage for data science, machine learning, or similar.
Advanced notebook workflows (e.g. creating web apps, reports, or APIs from notebooks).
Ability to interact with or contribute to open-source projects on GitHub.
In our team, ownership of problems is important – whether that’s by making progress on problems by debugging things yourself, or by asking for help in a good quality way (think good Stack Overflow questions) and then acting on it.
A mindset that enjoys balancing rapid business demands, the controls necessary in a financial environment, good quality engineering, and open source development would be likely to thrive.
What tasks might you be performing?
Develop an internal extension for our JupyterLab deployment, or contribute to an existing extension on GitHub.
Onboard an open-source analytics package (Python and/or JupyterLab).
Write internal production Python code for part of our Jupyter deployment in Athena.
Troubleshoot, work around, and upstream a fix for a bug in an open-source Python package.
Engage with business analytics, data science, and ML users to understand requirements, and help solve their problems by providing guidance on usage or extension of the analytics platform.
Integrate with cloud components such as Jupyter kernels running on public cloud.
Work on configuring and expanding shared pool of Jupyter servers on-prem.
Support “business as usual” usage of our Jupyter platform.
