Data Scientist

Job description

**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.