This opportunity is based in Zurich

(Internship) Integration of Privacy-Preserving Machine-Learning for Health Insurance Industry

Apply

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.

 

However, the insurance market in Switzerland is competitive and subject to strict data protection regulations. Different insurers (data controllers) may be unable or unwilling to share data about their customers (data subjects). In a previous internship, various distributed and privacy-preserving Federated Learning (FL) frameworks were explored and a first concept for integration was written.
 

By operationalizing Federated Learning approaches in the SUMEX Analytics Platform, we aim to offer new ML services to Swiss insurers that:

  • Provide high business-value.
  • Encourage potentially competing data controllers to offer data access.
  • Protect the data of the data subjects from unauthorized access.

In this role

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. You will work on a real-world problem and improve an important component of the SUMEX Analytics Platform to leverage FL solutions in a productive context.

 

What you will learn: You will take the role of a junior data scientist and help with the integration of a FL solution into the SUMEX Analytics Platform. To do so, you will explore the emerging field of privacy-preserving federated learning, as well as MLOps best practices and tools. You will evaluate different models and operationalize them in the SUMEX Analytics Platform.

  • Implement & Evaluate a ML Service based on existing FL technology.
  • Implement & Evaluate privacy preserving protocols.

What we offer

  • A dynamic work and collaborative environment with a highly motivated multi-cultural and multiples 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

  • Bachelors degree from a UNI, UAS (FH) or ETH, or Mid-Masters
  • already first small experiences in Coding
  • You are fascinated by Machine Learning, Distributed Computing, Probability Theory
  • You are familiar with following technologies: Python, ML Frameworks, Software Engineering
  • Willingness, and ability to understand the scientific background
  • Language skills in German and English

Inspire yourself by our motto: We make it work!


Interested? Then apply now, we look forward to getting to know you!

By continuing to browse this site, you accept the use of cookies or similar technologies whose purpose is to produce statistics on visits to our site (tests and measurement of visitor numbers, visit frequency, page views and performance) and to offer you content and promotions which will be of interest to you.

Our cookie policy has been updated. Please feel free to manage your preferences.

OK, accept all Disable all Configure
close
save

Manage your cookie preferences

Update your cookie preferences

Find out about the type of cookies stored on your device, accept or block them for the entire site, all services or on a service-by-service basis.

OK, accept all

Disable all

Visitor flow

These cookies provide us with insight into traffic sources and allow us to better understand our visitors anonymously.

(Google Analytics and CrazyEgg)

New

Sharing tool

Social media cookies allow content sharing on your preferred networks.

(ShareThis)

New

Visitor understanding

These cookies are used to track visitors across websites.

The intention is to enable us to offer more relevant, targeted content to existing contacts (ClickDimensions) and display ads that are relevant and engaging for users (Facebook Pixels).

 

New
For more information about these cookies and our cookie policy, click here