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 explicit 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.

📌 Data Scientist (Bengaluru)
🏢 Cyient
📍 Bengaluru