Data Scientist - Computer Vision

Job Title

Job Description

Key Responsibilities

AI/ML solution development

·                  Translate consumer, product and business challenges into well-defined Data Science and AI problems.

• Analyse complex and high-dimensional datasets and formulate hypotheses and proof-of-concepts.
• Design, develop and evaluate Machine Learning and Deep Learning models.
• Build solutions using Computer Vision, NLP, Generative AI and LLM-based techniques, depending on the use case.
• Evaluate state-of-the-art AI/ML approaches and apply appropriate techniques to complex business problems. 

Computer Vision & Deep Learning

·                  Develop AI-based Computer Vision applications for image-related use cases.

• Apply modern deep-learning architectures and frameworks, including approaches such as YOLO and U-Net, where appropriate.
• Work with image-processing and feature-extraction techniques and develop robust model-training and evaluation pipelines.

Generative AI & NLP

·                  Develop solutions using LLMs, Transformers, RAG architectures and agentic frameworks.

• Apply NLP approaches for extracting and representing information from unstructured data.
• Evaluate GenAI solutions for quality, reliability and suitability for the intended application. 

Data & experimentation

·                  Perform exploratory data analysis, data cleansing, preprocessing, feature engineering and statistical analysis.

• Ensure data quality, accuracy and reliability through validation and quality-control approaches.
• Establish reproducible methodologies and document experiments, assumptions, results and analytical findings. 

Productionisation & MLOps

·                  Participate in the end-to-end deployment and testing of Data Science solutions.

• Work closely with engineering teams to integrate AI models into production applications and enterprise workflows.
• Monitor and continuously improve deployed model performance.
• Contribute to robust and scalable data pipelines supporting advanced analytics and ML solutions. 

Collaboration & technical contribution

·                  Collaborate with Data Engineers, Software Engineers, product/business stakeholders and other cross-functional teams to operationalise Data Science solutions. 

• Communicate complex analytical results and recommendations clearly to stakeholders.
• Contribute to knowledge sharing, technical documentation and Data Science methodologies.
• Mentor Data Scientists and contribute to building Data Science competency within the organisation

You're the right fit if:

• 7-10 years experience in AI and Data Science field
• Master's in computers, electronics, electrical engineering or PhD in relevant streams
• Strong hands-on experience in several of the following:

Python and Data Science/ML ecosystem

• Machine Learning and statistical modelling
• Deep Learning

PyTorch / TensorFlow

Computer Vision and OpenCV

• Image processing and AI-based vision applications

NLP and Transformer

LLMs and Generative AI

Retrieval-Augmented Generation (RAG)

              Agentic AI frameworks

• Classical ML approaches such as regression, Random Forest, SVM and k-NN
• Statistical testing and experimental analysis
• Data preparation, validation and quality assessment

How we work together
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.
this role is an office role.
 

About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.
• Learn more about our business.
• Discover our rich and exciting history.
• Learn more about our purpose.
If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.

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