Intern Data Scientist

AI in this role

fine-tuning

Job Title

Job Description

Job Responsibilities:
• Collaborates extensively on AI DevOps activities, leveraging multiple tool sets at the solution level to streamline development, testing, inference, deployment, and monitoring processes with moderate complexity.
• Participates in discussions related to technical requirements, designs, and quality standards, providing inputs to ensure the robustness, scalability, and maintainability of AI solutions, working under direct supervision.
• Integrates deployable versions of machine learning models developed by data scientists into end products, ensuring compatibility, performance, and reliability of all system components.
• Handles data processing activities, including data cleansing, validation, and verification, to ensure the integrity and quality of data used for analysis and model training, with a focus on continuous improvement.
• Trains and retrains AI systems as necessary to adapt to changing data distributions, business requirements, and performance objectives, optimizing model performance through iterative refinement.
• Performs statistical analysis and fine-tuning of AI models using test results, hypothesis testing, and validation techniques to validate model assumptions, identify areas for improvement, and optimize model parameters.
• Contributes to activities within the on-site engineer team, providing the whole deployment plan and pipelines.
• Participates in the AI development process, working in pairing mode with equal team members, and actively contributing to requirements gathering, design discussions, code reviews, and quality assurance activities.
• Interacts with business, market, and IT stakeholders to formulate clear and actionable requirements for AI solutions, ensuring alignment with business goals, user needs, and technical capabilities.
• Ensures the quality of data and AI solutions developed, conducting thorough testing, validation, and verification activities to identify and address defects, errors, and performance issues before deployment.

Minimum required Education:
Master or PHD candidate in Biomedical Engineer, Computer Science, Information Management, Data Science, Artificial Intelligence, Applied Mathematics, Statistics or equivalent.

Minimum required Experience:
Experience with Data processing, Data Analytics, AI Modeling or equivalent.

Preferred Experience:
Experience in using programming languages such as Python, R, JAVA, C/C++

Preferred Certification:
Artificial Intelligence Board of America (ARTiBA) certified

Preferred Skills:
• Troubleshooting
• Data Analytics
• Statistical Methods
• Data Harmonization & Processing
• Artificial Intelligence (AI)
• Scripting & Automation
• AI Algorithm Development
• DevOps
• Data Governance
• LLM deployment

How we rate this

Intern Data Scientist at Philips rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine Tuning

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Fine Tuning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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