9 beste vacatures voor de functie van data scientist in den haag, zh (nieuwe medewerkers gezocht)

Machine Learning Engineer - Energy Management
Eneco —Rotterdam
- € 84.000 - € 117.000 per jaar
- Hybride werken
- AWS
- Continue verbetering
- Winstdeling
- Werk vanuit huis
C# Developer
Coolblue —Rotterdam
- Fulltime
- Informatica
- Oracle
- Informatica
- Reiskostenvergoeding
Full-Stack Data Engineer
Eneco —Rotterdam
- € 83.000 - € 117.000 per jaar
- Hybride werken
- DevOps
- Java
- Databases
- Winstdeling
- Werk vanuit huis
Machine learning Engineer
Sia —Rotterdam
- Fulltime
- Hybride werken
- Azure
- Ziekenhuis
- DevOps
Snel solliciteren
Software Developer | Data & AI‑oplossingen
Veneficus B.V. —Rotterdam
- € 3.250 - € 10.000 per maand
- Fulltime
- Hybride werken
- Informatica
- Azure
- Informatica
- Budget voor professionele ontwikkeling
Snel solliciteren
Stagiair AI Fairness & Ethics
Stedin —Rotterdam
- € 675 per maand
- Fulltime
- Engels
- Communicatieve vaardigheden
Lead Dev/Systems Engineer
SWIFT Financial Messaging Services —Leiden
- Fulltime
- Informatica
- Master
- Bachelor
Full-stack Software Engineer
Samotics B.V. —Leiden
- Fulltime
- Hybride werken
- AWS
- Bachelor
- Linux
Snel solliciteren
Senior Data Engineer - Telecommunicatie
CGI —Rotterdam
- Azure
- DevOps
- Engineering
- Flexibele werkuren
Snel solliciteren

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Machine Learning Engineer - Energy Management

Eneco -
Rotterdam

Nu solliciteren

Vacaturegegevens

€ 84.000 - € 117.000 per jaar

Secundaire arbeidsvoorwaarden

Winstdeling
Werk vanuit huis
Flexibele werkuren

Kwalificaties

CI/CD
Kubernetes
Spark
AWS
Docker
Continue verbetering
Continuous integration
Schaalbaarheid

Volledige vacaturetekst

- > 5 jaar

- Tech, Data & AI
- €84.000 - €117.000

Deel op social media:

Machine Learning Engineer - Energy Management

- Eneco - Rotterdam
- Tech, Data & AI
- > 5 jaar
- €84.000 - €117.000

Apply

Good to know: you can easily save this vacancy using the print button at the top of the page. After the closing date, this vacancy will be removed from our website.

Build production-grade machine learning solutions using Databricks, MLflow, AWS and modern MLOps practices.
Own the complete machine learning lifecycle, from scalable data processing and model training to production deployment and monitoring.
Collaborate in a multidisciplinary engineering team where Machine Learning Engineers, Data Scientists and Data Engineers build reliable, scalable AI solutions together.

Why choose Eneco?

At Eneco, we are committed to accelerating the energy transition. Through our One Planet strategy, we aim to become climate neutral by 2035 – together with our customers.

Digital technology and data play a key role in achieving this ambition. Within the Energy Management Systems domain, we develop reliable, scalable and intelligent data-driven solutions that support Eneco's digital products and services. As a Machine Learning Engineer, you will help bring machine learning solutions into production by building the platforms, tooling and engineering practices that enable models to deliver reliable business value.

Working closely with Data Scientists, Data Engineers and Software Engineers, you will build and operate production-grade machine learning solutions while helping shape the future of MLOps within Eneco.

What you’ll do

Design, build and maintain end-to-end machine learning pipelines for training, validation and production inference in both batch and real-time environments.
Develop scalable MLOps capabilities, including CI/CD pipelines, automated testing, model versioning, deployment strategies, drift detection and rollback mechanisms.
Build and operate Databricks-based data processing and machine learning workflows at scale.
Manage MLflow for experiment tracking, model registry and reproducible machine learning deployments.
Deploy, operate and optimize machine learning workloads on AWS while balancing performance, scalability and cost.
Develop scalable feature engineering pipelines, data contracts and feature stores, including Scala-based ETL pipelines where applicable.
Build monitoring, alerting and observability for production models, including model performance, latency, throughput, and data and model drift.
Work closely with Data Scientists to productionize machine learning models, provide feedback on production constraints and continuously improve deployed solutions.
Perform post-deployment analyses to evaluate and improve model performance and operational reliability.
Contribute to platform improvements, automation and MLOps best practices across the team.
Mentor fellow engineers and contribute to a strong engineering culture.

Is this about you?

You are passionate about building reliable production systems and enjoy bringing machine learning solutions into production. You combine strong software engineering skills with hands-on MLOps experience and thrive in multidisciplinary teams where engineering and data science come together.

4+ years professional experience in ML engineering; 2+ years owning MLOps/production ML systems.
Hands-on experience with Databricks (notebooks, jobs, clusters) for large-scale data processing and model training.
Production experience with MLflow for experiment tracking and model registry.
Strong AWS experience deploying and operating ML workloads.
Proficient in Scala for data processing/ETL on Spark; production-quality code and familiarity with JVM ecosystem.
Experience building CI/CD for ML and containerized deployments (Docker, Kubernetes).
Practical knowledge of model monitoring, A/B testing, canary deployments, and data/model drift detection.
Strong software engineering fundamentals: testing, design patterns, code reviews, and documentation.
Excellent communication and collaboration skills.

You’ll be responsible for

Build, deploy, and maintain end-to-end ML pipelines for training, validation, and production inference (batch and real-time)
Design and implement robust MLOps practices: CI/CD for models, model versioning, automated testing, drift detection, and rollback strategies.
Implement and operate Databricks-based data processing and model training workflows at scale.
Integrate and manage MLflow for experiment tracking, model registry, and reproducible runs.
Design, deploy, and optimize ML workloads on AWS with cost and performance tradeoffs.
Develop and maintain data contracts, feature stores, and scalable feature engineering pipelines (including Scala-based ETL where applicable).
Build monitoring, alerting, and observability for model performance, latency, throughput, and data/label drift.
Collaborate with data scientists to productionize models, provide feedback on model design for production constraints, and run post-deployment analyses.

This is where you’ll work

You will join a multidisciplinary team of Machine Learning Engineers, Data Scientists, Data Engineers and Data Analysts within the Energy Management Systems domain. Together, you develop, deploy and operate machine learning solutions that support Eneco's digital products and services.

Our team values collaboration, ownership and continuous learning. We encourage experimentation, knowledge sharing and continuous improvement while building reliable, scalable machine learning solutions that contribute to Eneco's ambition to accelerate the energy transition.

What we have to offer

Gross annual salary between €84.000 and €117.000

Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing.

FlexBudget

Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing. Have it paid out, use it to buy extra holiday days or save it up for something nice, it's up to you.

Personal and professional growth

Eneco is fully committed to help you in your personal and professional development.

Hybrid working: home, office or abroad

Work 40% at the office, 40% from home, and 20% flexibly. With manager approval, you may work abroad (within approved countries) up to 3 weeks/year, max 2 consecutively.

Want more information about our terms of employment?

Here's what you get from us

Work Where Everyone Matters

When you choose a career at Eneco, you choose ambition, growth, and opportunity in an environment where everyone matters. You’re given the space to develop yourself and to do your work in a way that suits you. We believe that different perspectives, nationalities, and backgrounds make us stronger, which is why we foster an open, safe and inclusive culture. Naturally, we also prioritize a healthy work-life balance, flexible working hours, and the option to work from home when your role allows it. If you have a physical or sensory disability, we will work with you to find the right adjustments so you can perform your job well.

This is how you build your own future and a sustainable future at the same time. Together with 4,000 colleagues, each with their own talents and ideas, you work on our shared mission: speeding up the energy transition. We help customers become more sustainable faster, create innovative solutions, and seize new opportunities. Will you join us?

The phases of our application procedure

Want to know more about this job function?

Contact our recuiter at: [email protected]

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data scientist vacatures in den haag, zh

Machine Learning Engineer - Energy Management

C# Developer

Full-Stack Data Engineer

Machine learning Engineer

Software Developer | Data & AI‑oplossingen

Stagiair AI Fairness & Ethics

Lead Dev/Systems Engineer

Full-stack Software Engineer

Senior Data Engineer - Telecommunicatie

Discover related jobs

Ik wil de nieuwste vacature-alerts ontvangen voor de functie van data scientist in den haag, zh

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Zoek vacatures in meer locaties

Machine Learning Engineer - Energy Management

Why choose Eneco?

What you’ll do

Is this about you?

You’ll be responsible for

This is where you’ll work

What we have to offer

Gross annual salary between €84.000 and €117.000

FlexBudget

Personal and professional growth

Hybrid working: home, office or abroad

Want more information about our terms of employment?

Work Where Everyone Matters

The phases of our application procedure

Want to know more about this job function?

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