Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?
No Offer Description We are looking for a Data Scientist to join our dynamic team and spearhead the technical aspects of our breast cancer diagnostic device development.
The ideal candidate will possess a strong technical background, a teamwork-oriented mindset, and a passion for driving innovation in healthcare.
Job Description * Lead improvements in AI algorithms based on processing and data analysis of clinical examinations gathered with the device, prioritizing sensitivity and specificity for challenging classes.
Design and implement preprocessing pipelines: signal denoising, normalization, scaling, and feature extraction.
- Keep our comprehensive dataset up to date, addressing data scarcity challenges.
- Lead troubleshooting efforts and provide technical expertise to resolve complex issues encountered during the development of AI models.
- Collaborate with our Head of Engineering and Clinical Lead in optimizing the quality of data readings obtained with our device.
- Collaborate with our QA/RA Lead to ensure that all technical aspects of the AI algorithm development adhere to quality standards, regulatory requirements, and industry best practices.
Our office space is at StartUB!
(Barcelona).
The current team is integrated by 8 people: CEO, Tech Lead, Operations Lead, Clinical Lead, QA/RA Manager, Project Manager, Lab Technician, Software Engineer.
Where to apply E-mail: ****** Requirements Research Field: Mathematics Education Level: Master Degree or equivalent Research Field: Computer Science » Programming Education Level: Master Degree or equivalent Skills/Qualifications * Advanced Degree in Computer Science, bioinformatics, physics, mathematics or related field.
- Proven track record of hands-on experience (=3 years), ideally in the development of algorithms of pre-processing codes to classify multidimensional time series data, preferably in the field of biology.
- Experience in cross-validation strategies and handling class imbalance.
- Strong collaborative mindset, open to iterative model refinement based on feedback from engineers and clinicians.
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