PyData Amsterdam 2026 — the speakers, verified

Amsterdam, Netherlands · September 2026 · organized by PyData Amsterdam / NumFOCUS

70 speakers below were extracted from the event's public agenda, and PyData Amsterdam 2026 publishes its own site.

Who spoke, and about what

• Iryna Kondrashchenko — Data scientist and co-founder of DataForce Solutions GmbH
  • Deterministic Orchestration for ML Experiments with Coding Agents source
• Oleh Kostromin — Data Scientist focused on Deep Learning and MLOps
  • Deterministic Orchestration for ML Experiments with Coding Agents source
• Theodore Meynard — Data Science Manager at GetYourGuide
  • Cold Start at Scale: Three Years of Experiments in a Travel Marketplace source
• Laura Summers — Lead Design Engineer at Pydantic
  • Mothering the Machine source
• Andy Kitchen
  • Mothering the Machine source
• Luca Baggi — ML Engineer @xtream
  • Inside the Mind of an LLM source
• Özge Çinko — Hello world! 👋 I'm Özge Çinko. I'm currently an AI Engineer at ING, working around agentic AI. Befo
  • LLM Evaluation in Production: A/B Testing and Observability source
• Kader Miyanyedi
  • LLM Evaluation in Production: A/B Testing and Observability source
• Cheuk Ting Ho — After having a career in Data Scientist and Developer Relations, Cheuk dedicated her work to the ope
  • Do you know how well your model is doing? Evaluate your LLMs source
• Anders Bogsnes
  • Taking Flight: Zero-Copy Data Transfer at Scale with Apache Arrow Flight and Friends source
• Jeroen Janssens — Jeroen Janssens, PhD, is a Senior Developer Relations Engineer at Posit, PBC. His expertise lies in visualizing data, im
  • The modern tool builder: CLIs, agents, and PEP 723 source
• Thijs Nieuwdorp — Thijs Nieuwdorp is the Lead Data Scientist at Xomnia in Amsterdam. His interest in the interaction between human and com
  • The New Polars Engine That Tackles Megabyte to Terabyte Workloads source
• Jay Alammar
  • From LLMs to Agents and from words to actions source
• Maarten Grootendorst
  • From LLMs to Agents and from words to actions source
• Giampaolo Casolla
  • From Query to Discovery: Building an AI Agent That Helps Travelers Explore source
• Steven Mi
  • From Query to Discovery: Building an AI Agent That Helps Travelers Explore source
• Belle Bruinsma
  • The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers source
• Akhila Vangara
  • The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers source
• Laura Israel
  • The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers source
• Konstantinos Tsoumas — Konstantinos is a data scientist currently working at Mars with over 3,5 years of experience in the Data Science industr
  • Maybe 3 Minutes, Maybe Chaos – when Conformal Prediction meets my commuting life source
• Jeroen Nelen
  • A/B Testing Plenary Debates in the Dutch Parliament with Multi-Agent AI using LangGraph source
• Dror A. Guldin
  • Your A/B Test Is Leaking: Practical Lessons in Measuring Network Effects source
• Nadieh Bremer
  • Amidst the Visualization and Art of Data source
• Leonardo Amorim
  • Beyond the Holdout: Mitigating Censoring Bias with Asymmetric IPW source
• Pauline van Nies
  • Beyond Benchmarks: Optimizing LLMs and Puzzle Agents for Cryptic Crosswords source
• Graziano Montanaro
  • DuckLake: The Lakehouse That Finally Embraces the Database source
• Kai Jeggle
  • Embed First, Predict Later: Energy forecasting from weather embeddings source
• Niels van Galen Last
  • When Context Breaks: Recursive Language Models with DSPy source
• Csanád Bakos
  • Real-time vs Batch Features for ML: Lessons from Fraud Detection at Scale source
• Marijn Markus
  • Enhance: Feeding the World with Data through Multi-Objective Optimization source
• Christiaan Erdbrink
  • A short tour of forgotten Machine Learning algorithms source
• Jodie Burchell — Dr. Jodie Burchell is the Developer Advocate in Data Science at JetBrains, and was previously a Lead
  • Reliable, rigorous, wrong: A psychometric view of LLM evals source
• Ricardo Angel Granados Lopez
  • Grounding AI Agents in Your Data Model source
• Schelto Crone
  • The unreasonable effectiveness of DAS: ML on fiber-optic vibration data for rail monitoring source
• Joost van 't Schip
  • The unreasonable effectiveness of DAS: ML on fiber-optic vibration data for rail monitoring source
• Thijs Bressers
  • Your dashboard is too late: Building real-time KPI alerting systems with Python source
• Rutger Lit
  • Scaling Two-Way Fixed Effects Models in Python with pyfixest: Lessons from Airline Pricing source
• Oz Mendelsohn
  • Distilling LLMs into Classical ML for 5,000+ Classes source
• Christophe Blefari
  • Analytics future source
• Alexander Kern
  • Data First, Model Second: Three Strategies for Production Computer Vision source
• Guus van der Ham
  • Data First, Model Second: Three Strategies for Production Computer Vision source
• Lin Jia
  • The A/B Testing Blind Spot: Solving the Opt-In Paradox with Randomized Encouragement and DoubleML source
• Kexin Fei
  • The A/B Testing Blind Spot: Solving the Opt-In Paradox with Randomized Encouragement and DoubleML source
• Martin Iglesias Goyanes
  • Trillion-Token Pretraining: Building a Foundational Model for payment data source
• Raúl Soutelo Quintela
  • Trillion-Token Pretraining: Building a Foundational Model for payment data source
• Azamat Omuraliev
  • When RAG is not enough: Architecting for 10M+ context windows source
• Thijs Sluijter
  • Rebuilding Picnic’s Recipe Recommender source
• Majid Hajiheidari
  • Rebuilding Picnic’s Recipe Recommender source
• Alex Litvinov
  • Answers you can question: building a verifiable AI analytics agent source
• Rayan Daod
  • Serving Personalized ML at Scale with Evolving Runtimes source
• Shehab Amin
  • Modernizing Spark: Performance Boost without Rewrite source
• Santosh Pingale
  • Modernizing Spark: Performance Boost without Rewrite source
• Sako Arts
  • Computer Use Beyond the Demo: Bringing Legacy Systems into the Agentic Era source
• Nicolai van der Smagt
  • Open Weights, Cloud Scale: Architecture Patterns for Faster and Cheaper Production Agents 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
  • Evaluating Agents at Scale: From 50 Examples to a Production Flywheel source
• Tara Farzami
  • When should an AI Agent say "I Don't Know"? Confidence, routing, and multi-turn evaluation in a LLM system source
• Borja Enrique Vilar Martos
  • Agent-Friendly Data Platforms: Semantic Layers, Tool APIs, and Guardrails for Agentic source
• Mohit Kumar
  • Systems for Scale: Architecting a Nationwide Energy Forecasting Platform source
• Daria Mustafina
  • Systems for Scale: Architecting a Nationwide Energy Forecasting Platform source
• Corné Vriends
  • Systems for Scale: Architecting a Nationwide Energy Forecasting Platform source
• Willem Feijen
  • Scheduling at Scale: Building a Railway Timetable Optimizer in Python source
• Merel Groen
  • Scheduling at Scale: Building a Railway Timetable Optimizer in Python source
• Matt Topol
  • DuckDB + ADBC: Faster, Easier Data Analytics source
• Emir Can
  • When Should AI Speak? Letting Agents Decide When It's Their Turn source
• Anna Pillar
  • When Should AI Speak? Letting Agents Decide When It's Their Turn source
• Luisa Orozco
  • When one score is not enough: matching real-world groundwater time series at scale source
• Patrick van Balkom
  • AI You Can Bet Your Business On: How Mars' Context-Lake Builds the Trust Bridge Between People and AI source
• Grégoire Martinon
  • Beyond LLM-as-Judge: Using GLIDE for Reliable, Scalable Evaluation of GenAI Systems source
• Ryan Marinelli
  • Stop Early, Decide Smarter: Bayesian Sequential Testing for LLM Benchmarking source
• Sadeeq Akintola
  • Reuniting the two distant cousins: Orchestrating your end-to-end Data Engineering Workflow Leveraging Python in Apache Beam and Apache Airflow source

Questions about PyData Amsterdam 2026

Who spoke at PyData Amsterdam 2026?

70 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 2026?

September 2026, 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?

70 of the 70 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.