We want to improve the lives of patients.

Every day. Worldwide.

Our goal is to sustainably improve the lives of patients with chronic kidney failure – with data, algorithms and artificial intelligence.

As a global innovation unit of Fresenius Medical Care, we use access to a unique data pool. We are the only company that combines development, operation and application of dialysis machines under one roof, and provides access to comprehensive technical and medical data.

We develop innovative solutions for patients, doctors and clinics. To this end, we are making our dialysis machines more intelligent, aiming at making better therapies available to all people worldwide in the future.

Our Team

Dr. Matthias Kuss Leadership

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Max Botler Data Scientist

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Lena Scherer Master's degree candidate

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Dr. Felix Brockherde Senior Data Scientist

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Aaron Pickering Director Data Science

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Dr. Jamie Ye Data Scientist

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Radomir Popovic Data Scientist

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Ritwika Mukherjee Data Scientist

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Nathan Warren Data Scientist

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Interviews

Dr. Matthias Kuss Leadership

Data Solutions is part of Global Research & Development at Fresenius Medical Care. We draw on a global network of excellent engineers and therapy developers and support them in making our devices even "smarter".

Max Botler Data Scientist

The most important aspect is certainly that our work is directly aimed at the well-being of the patients. On the technical level, we work along the entire pipeline, from the first concepts to the implementation of finished products.

Lena Scherer Master's degree candidate

If you have lots of ideas about what tomorrow's medical care should look like and are interested in simply trying out new approaches, this is the right place for you. Especially the combination of hardware and data science, the continuous exchange and involvement of patients offer many possibilities to really change something and to think ahead.

Dr. Felix Brockherde Senior Data Scientist

Firstly, we are analyzing the extent to which we can further individualize the therapy, for example, by making intelligent suggestions for therapy parameters to hospital staff or by using sensor data from previous therapies to detect thrombosis at an earlier stage. Secondly, we use sensor data to analyze the clinical process and thus make the therapy more comfortable for the patients.

Aaron Pickering Director Data Science

In data science there seems to have been a recent shift towards explainable and interpretable machine learning models. On a related note, causal inference also seems to be becoming more popular within the machine learning community. This trend is particularly applicable to the medical devices sector, where explainability is at a premium.

Dr. Jamie Ye Data Scientist

We are initiating some cool AI projects to drive the dialysis industry forward. I like the way how team members collaborate. We share knowledges, experiences and support each other. Information is transparent within the team. Every team member is highly motivated and we move towards the same goals.

Radomir Popovic Data Scientist

Some recent Natural Language Processing (NLP) advancements, which utilize Attention mechanism, seem to be quite powerful in wide range of other Machine Learning areas as well. As these models and algorithms get more sophisticated and accurate, I find interesting the challenge on how to optimally bridge the gap between them and the actual medical staff.

Ritwika Mukherjee Data Scientist

FMC Data Solutions is a team of cross-disciplinary, diverse, and enthusiastic data scientists. As part of the team, I get to build data-driven products to improve the quality of lives of patients and health care professionals. Using data analytics and interpretable machine learning models to impact healthcare is very important to me.

Nathan Warren Data Scientist

I enjoy being able to own my projects and know that I am creating solutions that work towards providing patients with better care. There is a tremendous amount of freedom, as we are encouraged to develop our own solutions, with the support of teammates.