(Internship/master Thesis) Privacy
vor 2 Wochen
SUMEX builds an innovative and market leading solution for Swiss medical invoice verification. Major accident and health insurers in Switzerland rely on SUMEX to detect mistakes and irregularities in medical invoices.
For our next-generation SUMEX Analytics Platform, we are going to offer machine learning (ML) services that are trained on the combined data of multiple insurers. This collaborative approach provides higher value than traditional ML models, which are trained on isolated data repositories.
With this initiative we aim to offer new ML services to Swiss insurers that:
- Provide high business-value.
- Encourage potentially competing data controllers to offer data access.
**Challenges**:This is a joint project of ELCA’s data science team and the SUMEX analytics team. You will closely collaborate with both. To be successful, you will need to learn about medical invoicing and understand the Analytics Platform’s architecture. It will be crucial to catch up with the current state of research. This will allow you to tackle at the same time the problems of data privacy and improving the performance of SUMEX’s ML services.
**What you will learn**:You will take the role of a junior data scientist exploring the emerging field of privacy-preserving federated learning. You will experiment with state-of-the-art ML approaches to tackle real-world problems. You will evaluate the models and assess how collaborative learning benefits the participants.
**Keywords**:Machine-Learning, Deep Learning, Federated Learning, Collaborative Learning, Distributed Learning, Data Security, Data Privacy
**In this role**:
- Design a PP-FL concept for the SUMEX Analytics Platform
- Implement & Evaluate a Prototype ML Service based on existing FL technology.
- (Extension) Implement & Evaluate privacy preserving protocols for the prototype.
**What we offer**:
- A dynamic work and collaborative environment with a highly motivated multi-cultural and international sites team
- Personal development through training and coaching
- A flat hierarchy and a culture of collaboration across all disciplines
- The chance to make a difference in peoples’ life by building innovative solutions
- High innovation and research backed up by collaboration with universities like EPFL
- Various internal coding events (Hackathon, Brownbags), see our technical blog
- Monthly After-Works organized per locations
- Good life balance (41 working hours per week and possibility to work 2 days per week from home)
**About your profile**:
- Knowledge: Machine Learning, Distributed Computing, Probability Theory
- Skills: Python, ML Frameworks, Software Engineering, Willingness and ability to understand the scientific background
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