Medior Data Scientist

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.

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.

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.

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.

• Strong proficiency in Python and the standard ML/data stack (such as scikit-learn, pandas, NumPy, and PyTorch or similar).

• A proven track record in statistics and probabilistic modeling, including topics such as Bayesian reasoning, hypothesis testing, probability distributions, and uncertainty quantification.

• Experience with causal inference, probabilistic graphical models, survival analysis, or similar methods for analyzing sequential or time-dependent processes.

• Comfort working with large, complex, and imperfect datasets — you know how to validate, clean, and reason critically about data quality.

• Strong communication skills and the ability to explain complex analytical topics to diverse technical and non-technical stakeholders.

• A proactive, hands-on, and pragmatic mindset with the ability to work independently in a multidisciplinary environment.

• Please note that you need to be authorized to work in the Netherlands — unfortunately, visa sponsorship is not available for this role.

• Experience working in a regulated industry such as medical devices, pharma, aerospace, or other safety-critical environments.

• Familiarity with predictive maintenance techniques, such as anomaly detection or remaining useful life (RUL) estimation on time-series sensor data.

• Experience with process mining, Markov models, or workflow analysis techniques.

• Exposure to computer vision, multi-modal sensor fusion, or medical imaging data.

• Knowledge of probabilistic programming frameworks such as PyMC.

• Experience with AWS data services (such as S3, Glue, Athena, or SageMaker) and cloud-based data infrastructure

We totally understand that and so are we! By joining us, you can play an active role in changing the future of health care. Excited to join our revolution? Then we’d love to hear from you! 

If you think you've got what it takes, but you don't meet all the requirements - we still recommend you apply. We would love to learn about your interest and experience, and consider if you can be a good fit for Vitestro. Nobody is a perfect fit, and we welcome applications with imperfections. We offer a learning environment where we grow together!