Internship – Product Engineering (Data Science: Machine Learning Analyst)

Job Title

Internship – Product Engineering (Data Science: Machine Learning Analyst)

Role Summary

Join the Product Engineering team in the In-Vehicle Networking product line to develop and validate anomaly-detection methods for machine learning models used on semiconductor production and test data. The role supports preparing new IC products for mass production by ensuring data and model predictions are reliable before downstream processing.

Experience Level

Entry-level internship. Typical assignment duration is 6 months or longer. Can be full-time or part-time (20–40 hours/week).

Responsibilities

Expected internship tasks include developing and validating methods and producing clear deliverables suitable for an internship and thesis.

• Design and evaluate anomaly-detection approaches to assess whether incoming data and model predictions are reliable.
• Analyze large-scale datasets to identify out-of-distribution behavior, distribution shifts, and input–output inconsistencies.
• Design and calibrate anomaly scoring mechanisms and decision thresholds balancing detection sensitivity with review capacity in production.
• Validate solutions using historical data and, where needed, by simulating or injecting anomalous scenarios.
• Collaborate with cross-functional teams (engineering, data science) to define anomaly types and translate findings into operational workflows.
• Document methodology, validation results, and recommended thresholds in a structured format for internship and thesis deliverables.

Requirements

Core technical and collaboration requirements. Candidates must be able to work independently and communicate results to technical stakeholders.

• Strong programming skills in Python and experience with data analysis or machine-learning workflows.
• Solid foundation in statistics and practical experience with anomaly detection, out-of-distribution detection, or distribution-shift analysis.
• Experience analyzing large datasets and validating models with historical or synthetic anomalous data.
• Ability to translate analytical results into practical engineering decisions and workflows.
• Interest in semiconductor test data and production analytics; willingness to work in an industrial production environment.
• Good communication and collaboration skills for cross-functional teamwork.
• Nice-to-have: experience deploying ML in production, familiarity with simulation/injection techniques, or domain knowledge of automotive in-vehicle networking data.

Education Requirements

Master's student in Computer Science, Machine Learning, Data Science, Statistics, or a closely related quantitative field (minor or coursework in Applied Mathematics is a plus). Must be registered as a student for the entire internship period. The internship can be combined with a thesis/graduation project.

About the Company

Company: NXP Semiconductors

Headquarters: Nijmegen, Netherlands

NXP Semiconductors N.V. is a global semiconductor company that provides High Performance Mixed Signal and Standard Product solutions. With over 45,000 employees and operations in more than 35 countries, NXP is a leader in secure connectivity solutions for embedded applications, catering to automotive, industrial IoT, mobile, and communication infrastructure markets. The company is committed to innovation and sustainability, advancing a smarter, safer, and more sustainable world through technology.