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Nijmegen
3 weeks ago
Internship – Product Engineering (Data Science: Machine Learning Analyst) logo

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

NXP Semiconductors

Nijmegen
3 weeks ago
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Internship – Product Engineering (Data Science: Machine Learning Analyst)

This internship position at NXP Semiconductors in Nijmegen, Netherlands focuses on developing anomaly detection for machine learning models in the Product Engineering team. The role involves analyzing large-scale datasets, designing anomaly scoring mechanisms, and collaborating with cross-functional teams. Candidates should be Master's students in Computer Science, Machine Learning, or related fields with strong Python skills.

AIOn-siteInternshipInternshipPythonData Analysis

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

This internship position at NXP Semiconductors in Nijmegen, Netherlands focuses on developing anomaly detection for machine learning models in the Product Engineering team. The role involves analyzing large-scale datasets, designing anomaly scoring mechanisms, and collaborating with cross-functional teams. Candidates should be Master's students in Computer Science, Machine Learning, or related fields with strong Python skills.

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AIOn-siteInternshipInternshipPython

Salary

Not specified

Work Location

Nijmegen, Gelderland, Netherlands, NL

Work Model

On-site

Employment Type

Temporary

Experience Level

Internship (Master's student level)

Contract Length

6 months

Core Qualifications

Technical (Must-have)
PythonData AnalysisMachine LearningStatisticsAnomaly DetectionOut-of-Distribution DetectionDistribution Shift AnalysisSemiconductor Test DataProduction Analytics
Soft Skills
CommunicationCollaboration

Preferred Qualifications

Technical (Nice-to-have)
Applied Mathematics

Key Responsibilities

  • •Develop and evaluate an anomaly detection approach for machine learning models.
  • •Analyze large-scale datasets to identify out-of-distribution behavior and data shifts.
  • •Design and calibrate an anomaly scoring mechanism and decision thresholds.
  • •Validate the solution using historical data and simulated anomaly scenarios.
  • •Collaborate with cross-functional teams to define anomaly types and interpret model behavior.
  • •Document methodology, validation results, and recommended thresholds.
InternshipData ScienceMachine LearningAnomaly DetectionSemiconductorPythonNXPNijmegenProduct Engineering
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