Applied Data Science Engineer (f/m/d)
About the Role
Location Germany Bayern Erlangen
1. Country: Germany
2. State/Province/County: Berlin
3. City: Berlin
4. Country: Spain
5. State/Province/County: Catalonia
6. City: Barcelona
7. Country: Portugal
8. State/Province/County: Lisboa
9. City: Lisbon
10. Country: Portugal
11. State/Province/County: Porto
12. City: Porto
13. Country: Spain
14. State/Province/County: Madrid
15. City: Madrid
Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy Global GmbH & Co. KG Organization EVP Global Functions Business Unit Digital Products and Solutions Full / Part time Full-time Experience Level Mid-level Professional A Snapshot of Your Day As an Applied Data Science Engineer within the Scalable Core team, you will work at the intersection of data science, software engineering, and AI/ML/GenAI innovation. You’ll design and implement intelligent, production-ready solutions for energy management by applying statistical models and building reusable, scalable components for our company-wide AI/ML/GenAI platform. Your contributions will directly enable energy management solutions across cloud, on-premises, and edge environments – supporting teams throughout Siemens Energy in delivering smarter, more efficient products. You’ll be surrounded by technical experts in scalable systems, DevOps, MLOps, and AI, all collaborating to shape the future of energy through software.How You’ll Make an Impact
16. Apply statistical and machine learning techniques to energy and industrial data to create predictive and prescriptive models.
17. Design reusable data science components for preprocessing, feature engineering, and statistical evaluation.
18. Collaborate with MLOps engineers to operationalize models in robust, scalable ML pipelines.
19. Contribute to experimentation workflows, model explainability, uncertainty quantification, and fairness evaluation.
20. Support automatic model selection, tuning, and lifecycle monitoring across environments (cloud, on-prem, edge).
21. Collaborate with multi-functional teams to translate business and engineering requirements into data-driven solutions.
What You Bring
22. Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related technical field
23. Extensive experience applying classical data science and statistical modeling to real-world challenges
24. Proficient in Python with hands-on expertise in pandas, scikit-learn, and NumPy
25. Skilled in ML/AI frameworks (, PyTorch, TensorFlow) with experience in experimentation and model evaluation
26. Knowledge of MLOps practices and tools such as MLflow, Airflow, or Kubeflow (preferred)
27. Bonus: Familiarity with LLMs, Generative AI architectures, or deploying models to edge environments