Product / Data Science Data Scientist Arnhem, Netherlands or Munich, Germany · Hybrid Medior (Master’s or PhD, 2–5 years’ experience) Full-time, permanent Languages View role

Nexus Grid; Arnhem; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

Nexus Grid, a DNV Company, is seeking a Data Scientist to turn raw signal data into actionable insights for grid operators. You will work on analytics and AI models behind PulseIQ, covering fault location, pre-fault detection, asset health and predictive analytics.

You will develop, validate and deploy models on time-series data from Smart Cable Guard installations, applying ML techniques to create new features and insights that drive product value.

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.
• Structured, curious approach and the ability to explain complex results simply.

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

Python

Time-series

Statistical modeling

Communication

Education

Master's or PhD in data science / EE / physics / math

Tools

scikit-learn

TensorFlow

PyTorch

Job description

Turn grid data into insight. Build models that matter. Help keep the power on.

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.

What you will do

• 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.

What success looks like

• Models are accurate, robust and used in production by customers.
• New insights and features reach the product and add measurable customer value.
• Data quality and analysis workflows are clear, reproducible and well documented.
• Customers and colleagues trust and understand the insights we deliver.

Role focus

Analytics, machine learning and AI

You will enjoy this role if you...

• like solving real-world problems with data
• are curious about signals, physics and patterns
• take ownership from idea to production
• communicate results clearly
• enjoy working in a small, close team

Potential growth path

Potential progression includes Senior or Lead Data Scientist, with opportunities to contribute to our AI roadmap, depending on performance and business needs.

What you bring

• A 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.

Valuable, but you can learn it here

• Knowledge of power systems, cable networks or signal processing.
• Experience deploying models to production and with MLOps practices.
• Experience with cloud platforms and data pipelines.
• Familiarity with large language models or other AI tooling.