Data Scientist - Medical Imaging (Plymouth, MN)

Philips•Plymouth, MN

12d•$120,487 - $192,780•Hybrid

About The Position

We are seeking a Data Scientist to join our AI/ML Software development team in developing state-of-the-art medical devices with a focus on medical image processing. As a Data Scientist, you will manage the oversight, governance, and quality control of medical imaging data used in developing and validating AI-driven models.

Requirements

Master’s degree (Ph.D. preferred) in Data Science, Information Systems, Health Informatics, Biomedical Engineering, or a related field.
Background in Medical Imaging or Healthcare Data.
Knowledge of privacy regulations, including HIPAA, GDPR, and medical device compliance (ISO 13485, FDA guidelines).
3+ years of professional experience, beyond education, as data scientist in a healthcare, life sciences, or medical device environment.
Strong understanding of medical imaging standards and formats (e.g., DICOM, NIfTI).
Familiarity with data management tools specific to imaging (e.g., PACS, cloud-based storage for imaging data).
Experience in managing medical imaging databases.
Understanding of how imaging data is used in AI/ML model development.
Proficient in Python and data management tools for organizing, storing, and maintaining imaging datasets, such as SQL, NoSQL, or specialized medical data storage systems.
Familiarity with data labeling platforms and tools for managing annotation processes (e.g., Labelbox, Supervisely).
Must be able to successfully perform the following minimum Physical, Cognitive and Environmental job requirements with or without accommodation for this position.

Responsibilities

Design and develop machine learning models and algorithms to provide recommendations or support clinical decisions.
Conduct model training, evaluation, and tuning for optimal results.
Build and evaluate models, perform statistical analyses, and provide insights, particularly in imaging data.
Use advanced analytics and machine learning techniques to identify patterns and trends.
Gather, process, and verify the quality, consistency, and reliability of raw data from various sources.
Identify and address data-related problems, such as data drift or incorrect preprocessing.
Collaborate with stakeholders to translate business requirements into data science projects.
Communicate findings and offers data-driven recommendations to stakeholders.
Validate models through statistical tests and cross-validation to ensure generalizability.
Document data sources, cleaning processes, and detailed descriptions of models, including assumptions, parameters, and performance metrics.