Synthetic Data & Analytics Intern - Business Solutions (m/f/n), Merl

Creos Luxembourg S.A. is the operator of electricity and natural gas networks in Luxembourg. Nearly 900 employees currently serve the company.

Creos is responsible for the planning, construction, maintenance, and operation of high, medium, and low voltage electricity networks, as well as high, medium, and low pressure natural gas pipelines that it owns or has been entrusted to manage. The networks managed by Creos include more than 12,000 km of electricity lines and approximately 2,200 km of natural gas pipelines, serving over 317,000 electricity customers and around 50,000 natural gas customers.

Tasks

As a Synthetic Data & Analytics Intern, you’ll work at the intersection of data science, analytics, and privacy engineering. Your mission: help design, generate, and evaluate synthetic datasets that enable realistic, GDPR-safe testing and analytics in our energy systems environment.

You’ll collaborate with Solution Architects, functional analysts, and IT stakeholders to:

• Build and evaluate synthetic data generation models
• Ensure data quality, consistency, and privacy compliance
• Translate technical findings into clear business insights

What You’ll Do

• Collect, clean, and preprocess structured datasets from internal systems
• Apply data normalization, missing-value handling, and data quality checks
• Perform exploratory data analysis (EDA) to uncover trends and correlations
• Compare synthetic vs. real datasets to assess representativeness and performance
• Document and visualize results through reports and dashboards
• Work closely with business and IT teams to ensure compliance and alignment with corporate goals

Profile

• Pursuing a Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or a related field
• Strong skills in Python (pandas, NumPy, matplotlib, etc.) for data manipulation and visualization
• Basic understanding of machine learning and deep learning (e.g., regression, classification, generative models)
• Knowledge of data profiling, statistical analysis, and data pipeline concepts
• Familiarity with synthetic data generation tools or cloud/MLOps environments (AWS, SageMaker) is a plus
• Excellent communication skills — you can explain technical findings to non-technical audiences
• Analytical, proactive, and comfortable working in cross-functional teams