Job Title

Data Scientist (ML/AI)

Job Description

Job Title:
Data Scientist - MR Service Innovation

Minimum Required Education:
Bachelor's Degree in Computer Science, Econometrics, Artificial Intelligence, Applied Mathematics, Statistics or equivalent.

Your Role:

Drive AI-powered intelligence across Philips MRI service operations, transforming reactive maintenance into a predictive, self-healing ecosystem that improves patient uptime worldwide.

You will be a foundational member of the Digital & AI team within MR Service Innovation, directly contributing to Philips Autonomous MR vision - building an MRI ecosystem that can detect, self-heal, predict and ultimately operate autonomously. Your models and analyses will influence how thousands of MRI systems worldwide are serviced, directly improving scanner uptime, patient throughput and hospital outcomes.
You will work in a lean, high-impact team led by the Director of Digital & AI, collaborating closely with R&D engineering, field service operations, MR business leadership and global service platform teams. You will partner with external delivery organisations and have visibility into executive-level decision-making across the MR business unit..
This role offers an exceptional growth trajectory: you will build hands-on expertise in healthcare AI within a regulated MedTech environment, develop business-case and stakeholder-communication skills alongside technical depth, and gain exposure to the full lifecycle of AI programmes - from data exploration through deployment. There is a clear pathway toward senior data science and AI leadership roles as the programme scales..
From flexible work arrangements to competitive health and wellness programmes, a career at Philips comes with all sorts of wonderful benefits. Be sure to speak to your recruiter about benefits in your region.

You're the Right Fit If:

1. Experience: You have built, validated and deployed machine learning or statistical models on real-world operational data. Exposure to service/maintenance analytics, time-series forecasting, NLP on unstructured technical text or IoT/sensor data is a strong differentiator. Experience working with large-scale CRM or ITSM datasets (e.g. Salesforce, ServiceNow) is valued.

2. Skills: You are proficient in Python and its data science ecosystem (pandas, scikit-learn, XGBoost, PyTorch or TensorFlow). You can work confidently with SQL, data visualisation tools and cloud platforms (AWS, Azure or GCP). You are comfortable translating analytical findings into business-language insights for non-technical stakeholders and can communicate complex results clearly.

3. Education: You hold a Bachelor's Degree or Vocational Education in Computer Science, Econometrics, Artificial Intelligence, Applied Mathematics, Statistics or an equivalent quantitative discipline.

4. Mindset: You thrive in ambiguity and are energised by building something from the ground up. You are curious about the healthcare domain, comfortable challenging assumptions with data, and motivated by the idea that your work will ultimately help keep MRI scanners running for the patients who depend on them.

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.
Onsite roles require full-time presence in the company's facilities.
Field roles are most effectively done outside of the company's main facilities, generally at the customers or suppliers locations.
Indicate if this role is an office/field/onsite 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.
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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 .

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