Siemens Energy
“We energize society” door onze klanten te ondersteunen in hun transitie naar een duurzamere wereld, met behulp van innovatieve technologieën en ons vermogen om ideeën tot werkelijkheid te brengen. Met bijna 100.000 medewerkers wereldwijd creëren wij de energie systemen van vandaag en morgen.
Over de functie
| Land | Staat/provincie/district | Stad |
|---|---|---|
| Spanje | Catalonia | Barcelona |
| Portugal | Lisbon | Lisbon |
As a Senior Applied Data Science Engineer within the Scalable Core team, you will lead the design and implementation of intelligent solutions that drive the digital transformation of energy management. You’ll apply advanced data science techniques to real-world problems, guide the development of reusable components for our AI/ML/GenAI platform, and ensure that deployed models are robust, scalable, and production-ready. Beyond technical contributions, you will play a key role in shaping the platform’s roadmap, mentoring junior team members, and aligning our work with strategic goals. Your impact will be felt across the Siemens Energy as you help engineering teams create high-quality, intelligent, and scalable software solutions for cloud, on-premises, and edge environments.
How You’ll Make an Impact
- Apply advanced data science techniques to complex energy datasets to extract insights and deliver robust, production-ready predictive solutions.
- Design and implement reusable AI/ML components (preprocessing, feature engineering, evaluation, monitoring) that power multiple energy use cases across the organization.
- Architect scalable integration of models into production systems in close collaboration with MLOps and platform engineers.
- Lead the end-to-end model lifecycle from experimentation to production deployment across cloud, on-prem, and edge environments, ensuring explainability, uncertainty estimation, robustness, and SLA compliance..
- Translate complex energy domain problems into structured, high-impact AI solutions with measurable business outcomes.
- Guide junior engineers, structure the team backlog, and align technical execution with overall platform strategy and priorities.
- Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related field.
- Solid longstanding hands-on experience in applied data science or statistical modeling, with strong practical outcomes.
- Advanced proficiency in Python and its data science ecosystem (e.g., pandas, scikit-learn, NumPy) and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
- Strong understanding of model evaluation, statistical analysis, and production deployment, including experience with MLOps tools like MLflow, Airflow, or Kubeflow across cloud, on-prem, and edge environments.
- Proven ability to lead, coach, and collaborate effectively within a cross-functional team, with excellent communication and stakeholder management skills.
- Experience working with large language models (LLMs) or generative AI systems is a plus
At Siemens Energy, we are more than just an energy technology company. With ~100.00 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.
Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.
- Competitive compensation package
- Work-life balance: Flexible working time
- Flexible mobile working policy (hybrid)
- Local benefits such as meal allowance, flexible plan and much more
- Self-driven development framework with insights and resources to develop and grow on technical and soft skills. Continuous learning
- International and cross Business Units