Power Grid Data Scientist | ML for Fault Detection

Nexus Grid Limited; Arnhem; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

Nexus Grid turns measurement data from power cables into insights that help grid operators prevent failures and act faster. As Data Scientist you work on the analytics and AI models behind our products, including PulseIQ, from fault location and pre-fault detection to asset health and prediction.

You are not working on abstract datasets. You work on real signals from real networks, and you see your models used by customers to make better decisions.

Qualifications

• Master’s degree or PhD in data science, electrical engineering, physics, mathematics or a related field.
• Experience developing and validating machine learning or statistical models.
• Strong programming skills in Python and common data science libraries.
• Experience working with time-series or sensor data.
• A structured, curious approach and the ability to explain complex results simply.
• Fluent English.

Responsibilities

• Develop, validate and improve models for fault detection, localization and asset condition assessment.
• Analyze large volumes of time-series and event data from Smart Cable Guard installations.
• Apply machine learning and AI techniques to create new insights and product features.
• Work with product, engineering and customer teams to turn models into reliable, usable features.
• Monitor model performance in production and improve accuracy over time.
• Communicate findings clearly to technical and non-technical stakeholders, including customers.

Skills

Machine learning

Python

Time-series data

Communication

English

Education

Master’s degree or PhD in data science or related field

Job description

Nexus Grid turns measurement data from power cables into insights that help grid operators prevent failures and act faster. As Data Scientist you work on the analytics and AI models behind our products, including PulseIQ, from fault location and pre-fault detection to asset health and prediction.

You are not working on abstract datasets. You work on real signals from real networks, and you see your models used by customers to make better decisions.