Munich Datageeks April Edition

We are thrilled to announce our next Meetup on April 23rd at E.ON.
Format:

• 2 talks (each ca. 40 min incl. discussion)
• Time for networking + food + drinks before, in between, and after the presentations
• Talks are held in English
• We will be taking photos and/or film footage at the event. These will be used to share news about our meetups and to publicize upcoming events.

The lineup:

First talk:
Linda Hong - Intelligently connected: Using AI algorithms for data enrichment in energy systems @E.ON

Abstract:
The information requirements for the energy infrastructure are continuously increasing from various sides, such as regulatory bodies or operational needs. Good data quality represents the foundation of accurate reports. At E.ON, we use AI algorithms to intelligently predict missing information to fully exploit our grid assets. Furthermore, we use modern data applications such as Databricks, hosted in the cloud.

Bio:
Linda works as a Senior Data Engineer at E.ON Digital Technology since 2024. In her role, she is working alongside E.ON's energy distribution companies on data and AI topics. Before E.ON, she worked as a Data Scientist at the Boston Consulting Group. She is a multilingual Data Engineer with 5+ years of professional experience in Energy, Industrial, and Banking projects in Finland, UK, and Germany. Her implementation work includes pricing, optimization, and time series projects.

Second Talk:
Samuele Mazzanti - How Yelp built a back-testing engine for safer, smarter ad budget allocation

Abstract:
This talk presents how a back-testing and simulation engine was built at Yelp to evaluate budget allocation strategies before deploying them to production. Rather than relying solely on live A/B tests, the system leverages historical data and controlled simulations to stress-test decisions, estimate trade-offs, and reduce risk. The session covers the motivation behind the system, its core design principles, and how it complements experimentation. It highlights practical lessons on building safer decision systems, aligning short-term metrics with long-term marketplace health, and moving from reactive experimentation to proactive decision modeling.

Bio:
Samuele Mazzanti is an ML Engineer at Yelp, with previous experience as a Data Scientist across diverse industries. His work focuses on decision systems, simulation, and causal machine learning. He is also a long-time technical writer, having published extensively on data science and decision science topics.