Job Description
About Vitestro
Founded in 2017 in Utrecht, Vitestro is pioneering the future of blood collection with the Aletta® Autonomous Robotic Phlebotomy Device™ (ARPD™). This groundbreaking medical device combines advanced multi-modal imaging (near-infrared, ultrasound, and Doppler ultrasound) with robotics and AI to perform the entire diagnostic blood draw procedure autonomously.
By addressing critical healthcare staffing shortages and improving patient experience, Vitestro is transforming one of the most common and essential medical procedures. With more than 90 team members and growing rapidly, we are scaling our impact. As we placed our first devices with customers, we are now expanding our team to ensure successful implementation and long-term reliability.
At Vitestro, we are committed to continuous innovation and improvement.
General Role Description
As our Medior Data Scientist, you play a key role in transforming complex device and procedural data into actionable insights that support product development, clinical validation, and operational improvements. You operate at the intersection of engineering, product, clinical, and data teams, developing probabilistic models and analytical frameworks that help guide decision-making across the organization. You actively collaborate with stakeholders to align on data quality, validation approaches, priorities, and scalable deployment of models.
First months at Vitestro
In your first months, your focus will be on understanding our device data, procedural workflows, and the KPIs that drive system performance. You will independently navigate large and imperfect datasets, align with stakeholders across engineering, product, clinical, and data teams, and establish effective ways of working for model development and deployment.
You will develop a probabilistic puncture model in a Jupyter Notebook environment to support hypothesis-driven testing of software improvements. Alongside this, you validate assumptions, improve data quality insights, and ensure analytical results are reproducible and clearly documented.
You will also strengthen analytical capabilities within the engineering organization by sharing practical data modeling techniques through hands-on sessions tailored to current analytical challenges.
This is what your success looks like
After 12 months, at least one of your models has been clinically validated through field testing and is actively used to support product and engineering decisions. Your models are accessible to - and trusted by - stakeholders across multiple disciplines and their documentation is of such quality that it allows independent usage. Moreover, they require only minimal maintenance effort.
You have also identified and validated an initial predictive maintenance signal using field data, resulting in at least one proactive maintenance action or design recommendation.
You thrive in a multidisciplinary environment where engineering, clinical, and product management teams collaborate closely. You enjoy translating complex and imperfect data into practical insights and bring a pragmatic, hands-on mentality to solving challenging problems.