Data Scientist

Required Experience:

• Minimum 3-5 years of experience in AI/ML, Data Science, or Applied AI Engineering.
• Strong proficiency in Python, machine learning, and data analysis.
• Hands-on experience with deep learning, transformer-based architectures, and modern AI frameworks (e.g., PyTorch, TensorFlow).
• Experience in one or more of the following domains: computer vision, generative AI, foundation models, diffusion models, predictive modelling.
• Developing, validating, and deploying AI/ML solutions in production or enterprise environments.
• Working with cross-functional teams comprising of domain experts, software engineers, product owners, and business stakeholders.
• Experience in medical imaging, healthcare AI, supply chain analytics, or other applied AI domains.

Required Skills:

1. AI Model Development:

• Computer vision
• Generative AI
• Predictive modelling
• Optimisation
• Model fine-tuning

2. Clinical Imaging AI:

• Medical imaging AI
• MRI image understanding
• Image quality assessment
• Model Explainability

3. Data Quality & Validation:

• Data cleansing
• Annotation review
• Dataset readiness
• Statistical validation
• Failure analysis

4. Deployment & MLOps:

• Experiment tracking
• Model versioning
• Deployment support
• Monitoring
• Documentation

5. Collaboration & Delivery:

• Cross-functional collaboration
• Requirement clarification
• Stakeholder interaction
• Technical documentation compliant with defined procedures and templates
• Productization support

Job Responsibilities:

• Collaborate with Philips teams on applied AI use cases across clinical imaging and supply chain domains.
• Contribute to the development, evaluation, and refinement of AI/ML models, including computer vision, generative AI, predictive modelling, and optimisation-based approaches, depending on the use case.
• Work with clinical, engineering, business, and IT stakeholders to translate problem statements into clear AI requirements, success criteria, data needs, and measurable outcomes.
• Support data preparation activities such as data cleansing, validation, quality checks, annotation review, dataset structuring, and readiness assessment to ensure reliable model development and evaluation.
• Develop, train, retrain, and fine-tune AI models using appropriate techniques, including statistical analysis, hypothesis testing, performance evaluation, explainability, and failure analysis.
• For clinical imaging use cases, contribute to image-based AI model development and validation, including MRI image generation, artefact detection, image quality assessment, and model explainability.
• For supply chain use cases, contribute to AI solutions involving forecasting, decision intelligence, workflow automation, and data-driven process improvement.
• Integrate AI models into deployable workflows or product environments in collaboration with software, platform, and DevOps teams, ensuring performance, scalability, reliability, and maintainability.
• Contribute to model lifecycle activities, including experiment tracking, model versioning, monitoring, documentation, validation evidence, and continuous improvement.
• Participate actively in design discussions, code reviews, testing, quality assurance, and knowledge-sharing activities, while working closely with Philips team members in a collaborative delivery model.