Tech Lead Data Science and Engineering

vor 5 Monaten

Berlin, Deutschland Fresenius Medical Care Vollzeit

As a Tech Lead in the Data Solutions team of Fresenius Medical Care, you will play a crucial role in leading the development and implementation of data solutions for the technical operation of medical devices, contributing to enhanced patient care and operational efficiency.

**Your assignments**:

- Take technical ownership of a team of data scientists and engineers, leading the design, implementation, and management of cloud infrastructure, machine learning models, and algorithms to extract insights from complex medical data.
- Collaborate closely with the Head of Data Science and Head of Data Engineering to shape the technical development direction and make architectural decisions.
- Consult stakeholder on data analytics solutions and their feasibility for addressing critical medical challenges.
- Utilize data, quantitative methods, and software to analyze and visualize machine sensor data, enabling predictive maintenance and smart diagnosis products from initial implementation to delivery.
- Contribute to code, research, model development, data analysis, code reviews, and system architecture, setting high standards of technical excellence.
- Work collaboratively with business and functional teams to drive the realization of the digital and data strategy of Fresenius Medical Care’s MedTech sector.
- Foster a team culture of technical excellence and value-based product delivery.
- Manage external partners and researchers.

**Your profile**:
**Critical Experience**
- Proven track record in data science or engineering leadership with experience in leading data science/engineering teams for end-to-end product delivery
- Minimum 4 years of experience in a technical data role
- Understanding of data science, data engineering, and cloud architecture with expert knowledge in one of these disciplines

**Nice to have**:

- Background in working with machine sensor data, leveraging insights for medical device optimization.
- Experience with building models for predictive maintenance and smart services, enhancing reliability and operational efficiency.

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