PyData Amsterdam 2025 — the speakers, verified

Amsterdam, Netherlands · September 2025 · organized by PyData Amsterdam / NumFOCUS

66 speakers below were extracted from the event's public agenda, and PyData Amsterdam 2025 publishes its own site.

Who spoke, and about what

• Iryna Kondrashchenko — Data scientist and co-founder of DataForce Solutions GmbH
  • Is Prompt Engineering Dead? How Auto-Optimization is Changing the Game source
• Oleh Kostromin — Data Scientist focused on Deep Learning and MLOps
  • Is Prompt Engineering Dead? How Auto-Optimization is Changing the Game source
• Gabriele Orlandi — Data Scientist @ xtream
  • Model Context Protocol: Principles and Practice source
• Jeroen Janssens — Jeroen Janssens, PhD, is a Senior Developer Relations Engineer at Posit, PBC. His expertise lies in visualizing data, im
  • Actionable Techniques for Finding Performance Regressions source
• Thijs Nieuwdorp — Thijs Nieuwdorp is the Lead Data Scientist at Xomnia in Amsterdam. His interest in the interaction between human and com
  • Actionable Techniques for Finding Performance Regressions source
• Adam Hill — Adam is the Interim Director of Data Science at ComplyAdvantage, where he leads a brilliant team tackling financial crim
  • Bridging the Gap: Building Robust, Tool-Integrated LLM Applications with the Model Context Protocol source
• Konstantinos Tsoumas — Konstantinos is a data scientist currently working at Mars with over 3,5 years of experience in the Data Science industr
  • Uncertainty Unleashed: Wrapping Your Predictions in Honesty with Conformal Prediction source
• Bauke Brenninkmeijer — I’m an experienced AI engineer, having built ML and AI projects in a variety of industries. After working for several st
  • Context is King: Evaluating Long Context vs. RAG for Data Grounding source
• Hannes Mühleisen — Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs,
  • Minus Three Tier: Data Architecture Turned Upside Down source
• Javier de la Rúa Martínez — Javier is a Research Engineer at Hopsworks where he actively contributes to advancing the
  • Composable Pipelines for ML: Automating Feature Engineering with Hopsworks’ Brewer source
• Aarti Jha — Aarti Jha is a Senior Data Scientist at Red Hat, where she develops AI-driven solutions to streamline internal processes
  • Next-Level Retrieval in RAG: Techniques and Tools for Enhanced Performance source
• Mahima Arora — Mahima Arora is a Senior Data Scientist on the Data & AI team at Red Hat, specializing in Generative AI applications. Sh
  • Next-Level Retrieval in RAG: Techniques and Tools for Enhanced Performance source
• Yaroslav Sokolov — Yaroslav Sokolov leads the development of the AI Toolkit at JetBrains. He previously worked as a machine learning engine
  • Building AI Agents With Observability Tooling in PyCharm source
• Lenar Sharipov — Lenar Sharipov is a Tech Lead at JetBrains, working on the AI Toolkit team. He builds tools for PyCharm and other IDEs t
  • Building AI Agents With Observability Tooling in PyCharm source
• Shourya Sharma — I like to build smart AI systems that solve smart business problems.
  • Bridging the Gap: Building Robust, Tool-Integrated LLM Applications with the Model Context Protocol source
• Dana Arsovska — Dana is a Platform Engineer at Ahold Delhaize, where she works on the technical and organizational challenges of buildin
  • Event-Driven AI Agent Workflows with Dapr source
• Marc Duiker — Marc is a Sr Developer Advocate at Diagrid and enjoys sharing knowledge on how to build distributed applications. He's o
  • Event-Driven AI Agent Workflows with Dapr source
• Panos Vagenas — AI Engineer at IBM Research, leading development efforts at the intersection of Artificial Intelligence, Information Ret
  • Meet Docling: The “Pandas” for document AI source
• Mingxuan Zhao — Mingxuan Zhao
  • Meet Docling: The “Pandas” for document AI source
• Panos Alexopoulos — Panos Alexopoulos is a semantic technologies expert with nearly two decades of experience in knowledge graphs, ontology
  • Grounding LLMs on Solid Knowledge: Assessing and Improving Knowledge Graph Quality in GraphRAG Applications source
• Tomek Roszczynialski — Tomek is a data scientist and machine learning developer with a background in, and a passion for, physics.
  • Listen: A Practical Introduction to Data Sonification source
• Sven Arends — Sven Arends is a Senior Machine Learning Engineer at Picnic, where he is currently developing novel LLM applications. Wi
  • Counting Groceries with Computer Vision: How Picnic Tracks Inventory Automatically source
• Maarten de Ruiter — Maarten de Ruiter is a Data Scientist at Xomnia, specializing in developing and deploying GenAI applications, most recen
  • GenAI governance in practice: patterns, pitfalls & strategies across tools and industries source
• Jaap Stefels — Machine Learning Scientist at Adyen
  • No labels? No problem! - Hunting Fraudsters with Minimal Labels and Maximum ML source
• Itzel Belderbos — Machine Learning Scientist at Adyen
  • No labels? No problem! - Hunting Fraudsters with Minimal Labels and Maximum ML source
• Antonino Ingargiola — Antonino Ingargiola is currently Lead AI Architect at Agile Lab where he oversees AI initiatives and projects in large e
  • Large-Scale Video Intelligence source
• Irene Donato — Irene Donato is a Data Scientist at Agile Lab with a PhD in Mathematics and a background in Physics. She specializes in
  • Large-Scale Video Intelligence source
• Dick Abma — As a physicist, I enjoy solving real-world problems. My analytical approach is based on years of experience in modelling
  • Potato breeding using image analysis in a production setting source
• Rik Nuijten — Rik Nuijten is a Data Scientist at Solynta (a hybrid potato breeding company). His expertise lies in geospatial analysis
  • Potato breeding using image analysis in a production setting source
• Benjamin Bossan — Machine Learning Engineer at Hugging Face
  • Designing tests for ML libraries – lessons from the wild source
• Sayak Paul — Sayak works on diffusion models at Hugging Face. His day-to-day includes training and babysitting diffusion models for i
  • Designing tests for ML libraries – lessons from the wild source
• Paul verhaar — With a background in computational linguistics and a strong link to academia, Paul leads our DS & AI team. He focuses on
  • Measure twice, deploy once: Evaluation of retrieval systems source
• Marten koopmans — After a PhD in computational physics, Marten transitioned from modelling solar cells to evaluating ML systems. He now wo
  • Measure twice, deploy once: Evaluation of retrieval systems source
• Florenz Hollebrandse — Florenz Hollebrandse is a senior leader in Data & Analytics at ING, one of the largest banks in Europe. In his role he i
  • Flip the Plan: Fast-Track Your AI/ML Model Integration with a Back-to-Front Implementation Strategy source
• Simon Brugman — Simon Brugman is a Lead Data Scientist based in Amsterdam, currently working at ING Wholesale Banking Advanced Analytics
  • Streamlining data pipeline development with Ordeq source
• Niels Neerhoff — Niels has been a software engineer at ING for over four years, and currently focuses on data products for ESG. Previousl
  • Streamlining data pipeline development with Ordeq source
• Denis Gaitan — Denis Gaitan is an accomplished IT Specialist with a history in the software industry since 2012. Over the past five yea
  • Continuous monitoring of model drift in the financial sector source
• Agustin Iniguez — Agustin Iniguez is a data scientist with a background in physics. For the past few years, he has been working at Raboban
  • Continuous monitoring of model drift in the financial sector source
• Danica Fine — Danica began her career as a software engineer in data visualization and warehousing with a business intelligence team w
  • Quiet on Set: Building an On-Air Sign with Open Source Technologies source
• Omar Hommos — Engineering Lead at Adyen
  • Leading through the GenAI hype cycle: the good, the bad, and the ugly source
• Judith Redi — CTO at Creative Fabrica
  • Leading through the GenAI hype cycle: the good, the bad, and the ugly source
• Ninghang Hu — Director of AI & Ad Tech at Tencent
  • Leading through the GenAI hype cycle: the good, the bad, and the ugly source
• Judith Dijk — Judith Dijk has more than 25 years of experience in the field of Imaging and Artificial Intelligence. The focus of her r
  • Image processing, artificial intelligence, and autonomous systems source
• Vincent Warmerdam — Vincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You mi
  • Untitled13.ipynb source
• George Chouliaras — Experienced AI practitioner with more than 7 years of experience in building and deploying AI systems. I have a particul
  • Scaling Trust: A practical guide on evaluating LLMs and Agents source
• Antonio Castelli — Senior Machine Learning Scientist @Booking.com
  • Scaling Trust: A practical guide on evaluating LLMs and Agents source
• Maria Bader — As a Senior Data Scientist at Mollie, Maria has transitioned from data nerd to AI whisperer. By delivering many AI solut
  • How to Keep Your LLM Chatbots Real: A Metrics Survival Guide source
• Iva Gornishka — Iva is a data scientist at the City of Amsterdam, where she researches the responsible use of AI for municipal use cases
  • Evaluating the alignment of LLMs to Dutch societal values source
• Laurens Samson — Laurens Samson leads a development team at the City of Amsterdam that guides the implementation of LLMs across municipal
  • Evaluating the alignment of LLMs to Dutch societal values source
• Manu Joseph — Manu S.Joseph is a Software Engineer at Hopsworks, where he works on advancing the Hopsworks AI LakeHouse. He did his Ma
  • Real-Time Context Engineering for LLMs source
• Fabio Lipreri — I am a Data Scientist with a computer science background. I love solving hard problems using math, statistics and machin
  • Model Context Protocol: Principles and Practice source
• Evertjan Peer — Evertjan Peer is a Tech Lead at KickstartAI, where he focuses on delivering business impact through accelerating AI adop
  • Detection of Unattended Objects in Public Spaces using AI source
• Vitalie Spinu — As a machine learning scientist at Adyen, my current focus lies in the development of models and explainability tooling
  • Optimal Observability: Partitioning Data into Time-Series for Enhanced Anomaly Detection and Improved Monitoring Coverage source
• Muhammad Chenariyan Nakhaee — I am an AI specialist at Exact, where I work with Python during the day. In my spare time, I explore my true passion for
  • Searching for My Next Chart source
• Raphael Mitsch — I'm a machine learning engineer, these days mostly working in natural language processing. I have soft spots for data vi
  • Sieves: Plug-and-Play NLP Pipelines With Zero-Shot Models source
• Arda Kaygan — Arda is a data scientist, comedy enthusiast, and a self-proclaimed comfort-zone escaper. Originally from Turkey, he grad
  • Techie vs Comic: The sequel source
• Shimanto Rahman — Hi, I am Shimanto Rahman a PhD student at Ghent University. In my PhD I specialize in measuring machine learning models
  • Optimize the Right Thing: Cost-Sensitive Classification in Practice source
• Vitalii Zhebrakovskyi — Vitalii Zhebrakovskyi is Senior Software Engineer at Adyen working in fraud prevention and MLOps fields. He's the major
  • Declarative Feature Engineering: Bridging Spark and Flink with a Unified DSL source
• Miguel Leite — I’m an ML Scientist fighting fraud at Adyen. I also help implementing software engineering best practices in ML projects
  • Declarative Feature Engineering: Bridging Spark and Flink with a Unified DSL source
• Dima Baranetskyi — Dima Baranetskyi is a Technical Lead and Senior Data Engineering Consultant with a background in software and data engin
  • Kafka Internals I Wish I Knew Sooner: The Non-Boring Truths source
• Inge van den Ende — At Dexter Energy, Inge, a data scientist, is developing machine learning-powered products for short-term power trading o
  • Kickstart Your Probabilistic Forecasting with Level Set and Quantile Regression Forests source
• Oscar Ligthart — I am a technical lead within the data platform team at Vinted, building tooling to empower our users to create data prod
  • Orchestrating success: How Vinted standardizes large-scale, decentralized data pipelines source
• Rodrigo Loredo — With over 8 years of experience working with data, I've explored various roles. Currently, I work as a Lead Analytics En
  • Orchestrating success: How Vinted standardizes large-scale, decentralized data pipelines source
• Gergely Daroczi — Gergely Daroczi, PhD, is a passionate R/Python user and package developer for two decades. With over 15 years in the ind
  • Resource Monitoring and Optimization with Metaflow source
• Pablo Estevez — Pablo Estevez leads Data and Machine Learning in Eneco’s Energy Trading teams. For more than ten years he has worked acr
  • Data that Keeps Our Energy in Balance - From churn prediction with deep learning to real-time trading systems source
• Manolis Manousogiannis — Manolis Manousogiannis is a Senior Data Engineer in Eneco's Energy Trading team, specializing in distributed data proces
  • Data that Keeps Our Energy in Balance - From churn prediction with deep learning to real-time trading systems source

Questions about PyData Amsterdam 2025

Who spoke at PyData Amsterdam 2025?

66 speakers we hold a record for, listed below with the session each one gave. The list comes from the conference's own published agenda, so it is who actually appeared rather than who applied or who paid.

When and where was PyData Amsterdam 2025?

September 2025, in Amsterdam, run by PyData Amsterdam / NumFOCUS. The agenda it was taken from is linked at the top of this page.

Are the talk titles real?

66 of the 66 records carry the session title exactly as the agenda printed it, so every one below is quoted.

Can I contact these speakers?

Not through us. We hold no contact details and no fees. Each entry links to the agenda page it came from, and that is the route.