About us

IDOVEN is a health tech startup seeking to prevent cardiac disease through AI. With our breakthrough AI-based electrocardiogram analysis platform, we work with physicians and healthcare leaders to enable early detection and diagnosis at scale.

This is a unique opportunity. With the backing of the best investors in the industry, the European Innovation Council (EIC) and Horizon 2020 European Commission, you'll have the responsibility and resources to shape the creation of a game-changing product that can save millions of lives.

Our technology is recognized with the European Seal of Excellence, supported by EIT Health (a network of best-in-class health innovators backed by the EU), and received the Healthy Longevity Catalyst Award from the U.S. National Academy of Medicine. In 2021, we were awarded at South Summit as Most Disruptive Startup and Best Health Startup. We are one of the most promising European technology companies to watch.

About you

As a Data Scientist at Idoven, you will play a crucial role in developing and implementing machine learning algorithms and models to analyze cardiovascular data. You will work closely with cross-functional teams, including software engineers, clinicians, and product managers, to translate complex data into actionable insights.

You will be responsible for the following:

• Data Analysis and Modeling: Develop, implement, and optimize machine learning models for analyzing cardiac data. This includes data preprocessing, feature engineering, and model selection.
• Algorithm Development: Create and refine algorithms to improve the accuracy and efficiency of heart health diagnostics.
• Research and Innovation: Stay updated with the latest research in machine learning, AI, and cardiovascular health to continuously enhance our products.
• Collaboration: Work collaboratively with other teams to integrate models into the production environment and ensure they meet clinical standards.
• Data Visualization: Present data findings in a clear and concise manner to stakeholders through dashboards, reports, and presentations.
• Performance Monitoring: Monitor the performance of deployed models and conduct testing to validate their effectiveness.
• Compliance and Security: Ensure all data handling processes comply with regulatory standards and maintain high levels of data security and privacy.