Data Scientist

Zurich - Switzerland

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

About Lyceum

Lyceum is building a user-centric GPU cloud from the ground up. Our mission is to make high-performance computing seamless accessible and tailored to the needs of modern AI and ML workloads. Were not just deploying infrastructure were designing and building our own large-scale GPU clusters from scratch. If youve ever wanted to help shape a cloud platform from day one this is your moment.

Tasks

The Role

Were hiring a user-obsessed Data Scientist with Life Sciences experience to bridge real bio/health workflows and our product. Youll partner with users and customers to understand and integrate deployment workflows to make our platform the default compute backbone for bio/health AI.

What Youll Do

Understand: analyze end-to-end LS pipelines and pinpoint pain points
Translate: convert user insights into crisp specs and priorities
Validate: define reference workflows/benchmarks and measure impact
Activate: create quickstarts/guides; improve onboarding & success metrics
Accelerate: identify bottlenecks and turn perf wins into product

Requirements

What Were Looking For

Life Sciences background (bioinformatics comp bio medical imaging chem/ADMET)
Strong Python/ML and a sound experimentation/evaluation mindset
Data fluency (SQL/analytics) reproducibility pragmatic pipeline experience
GPU fundamentals (batching memory/throughput)
Clear communication: turn feedback into tight specs & great docs

Bonus Points

Experience with large-scale single-cell digital pathology or protein/chem tasks
Familiarity with clinical/RWD contexts data governance & QA standards
Public tech writing or OSS contributions

Lyceum is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees

About LyceumLyceum is building a user-centric GPU cloud from the ground up. Our mission is to make high-performance computing seamless accessible and tailored to the needs of modern AI and ML workloads. Were not just deploying infrastructure were designing and building our own large-scale GPU cluste...