Data Scientist

Sansid Solutions; Bengaluru; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

Philips in Bengaluru is seeking an experienced AI/ML engineer to drive applied AI initiatives across clinical imaging and supply chain domains. You will develop, evaluate, and refine AI models, including computer vision and generative AI, while collaborating with clinical, engineering, business and IT stakeholders to translate problems into measurable AI requirements.

The role involves data preparation, training and fine-tuning models, and integrating AI solutions into deployable workflows with

Qualifications

• 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 computer vision, generative AI, foundation models, diffusion models, predictive modelling.
• Developing, validating, and deploying AI/ML solutions in production or enterprise environments.

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.

Skills

AI model development

Clinical imaging AI

Data quality & validation

Deployment & MLOps

Collaboration & delivery

Tools

Python

PyTorch

TensorFlow

Job description

Immediate Joiner who can join on or before 30th August 2026

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.