PyData Amsterdam 2026

​Alexander Kern is AI Solutions Expert at Clockworks where he works on numerous computer vision solutions that derive structured insights from raw pixel data. His work exemplifies how AI should be combined with smart engineering to deliver solutions that always and reliably work in production.

Anna Pillar is a data scientist at Sogeti. With a foundation in both Cognitive Science and AI, she combines technical knowledge of AI with insights from theory of mind and social cognition. Day to day, she puts this into practice building LLM-powered solutions, including agentic workflows.

Outside of work, she is an avid reader, board game enthusiast and proud owner of a sourdough starter.

Arian Pasquali is a Research Engineer at Orq.ai, specialized in AI agents and LLM evaluation. Before joining Orq.ai, he spent years in consulting and doing research in the academia, building an extensive publication record in natural language processing and information retrieval.

Arkadii Bessonov is an LLM Engineer specializing in large-scale model training infrastructure. His work focuses on production-scale pretraining.

Auxten

• 👨🏻‍💻 Experience in RecSys, Database
• Technical Director of ClickHouse core team

• Principal Engineer in Shopee (ML Platform)

• ❤️ Love Open Source!

• Contributed to ClickHouse, Jemalloc, K8s, Memcached, CockroachDB, Superset
• Creator of chDB(Acquired), CovenantSQL

Solutions Architect at Databricks.

Helping Healthcare and Life Sciences enterprises accelerate on their data+AI journey. In past lives worked as MLE at a bank, AI engineer at an early stage startup and ran a non-profit in Kyrgyzstan.

Played with GPT-3 before ChatGPT came out.

AI Research Engineer at orq.ai, where I build systems that make LLM agents observable and improvable — trace analysis, clustering, failure taxonomies, evaluation. Recently published work on red teaming AI agents: a capability-aware framework for finding security vulnerabilities in tool-using agents before attackers do.

Previously spent 5 years at ING and ABN AMRO shipping ML into production, and before that, startups where every hat was mine. I co-organize the MLOps Community Amsterdam meetup.

Find me to talk about LLM evaluation, agent security, or why production is where the interesting problems live.

TBD

As an experienced data professional, Christiaan’s interests range from data science and predictive modelling, to the engineering behind AI and ML systems.
After specialising in hydrodynamics, he worked for several years at Deltares as a consultant on the design of hydraulic structures in The Netherlands. His PhD research on vibrations induced by turbulent flows led him to adopt machine learning techniques early on.
Subsequent data science employment in the UK included Internet-of-Things applications at Centrica, and payments optimisation — most recently at Worldpay. Having founded and managed both data science and MLOps teams, he is now freelancing in Amsterdam while also working on personal projects.
Christiaan holds an MSc in Civil Engineering from the TU Delft and a PhD in Computational Science from the University of Amsterdam.

tbd

Csanád is a Data Engineer at Vinted, where he builds and maintains dozens of Apache Flink streaming jobs powering real-time feature engineering for Trust & Safety. His work ensures that ML models and rule engines have timely, complete data to detect malicious users on Europe's largest second-hand marketplace — driving thousands of automated actions per hour including account blocks, listing removals, and manual review escalations. He has spoken at Confluent's real-time data community meetup in London and he’s a presenter at Current 2026 London on custom Flink operators for feature-trigger synchronization. Csanád holds a Master's degree in Computer Science from the Delft University of Technology, where he graduated cum laude.

Dror A. Guldin is a Senior Staff Data Scientist at Meta, where he focuses on experimentation methodology and product analytics for networked products since 2018.

Based in Amsterdam, he spoke at PyData Amsterdam 2023 on metrics and KPIs. These days he spends a lot of time thinking about what happens to data roles when AI gets good enough to do most of the execution, and whether that's exciting or terrifying (current verdict: probably both).

I am a Senior Data Scientist/AI Engineer with a passion for building AI systems that actually work in the real world. With a background in aerospace engineering, I tend to approach AI problems as systems rather than isolated models, focusing on how different components interact, fail, and improve together.

My recent work focuses on RAG system, generative AI, and the practical challenges of making AI collaborate effectively. I am particularly interested in questions that go beyond model accuracy, such as coordination, evaluation, architecture and decision-making in complex AI systems.

Outside of my professional work, I enjoy rowing and coaching, and I actively mentor junior data and AI professionals who are looking to grow in the field. I also like sharing insights through presentations/talks and hands-on projects, with the goal of making advanced AI concepts more practical and accessible.

Giampaolo Casolla is a Senior Data Scientist at GetYourGuide, leveraging advanced machine learning and Generative AI to solve complex travel industry challenges. With expertise spanning areas like Safety, Risk, and Security, and strong skills in stats, Python, R, and cloud tech, he brings a diverse background to the role. Prior to GetYourGuide, Giampaolo developed award-winning ML solutions at Amazon and has a background in research with publications and conference presentations. At GetYourGuide, he's focused on integrating LLMs and GenAI into data products to drive innovation in travel technology.

Currently working as a Machine Learning Engineer at Intella.

Interested in end-to-end Machine Learning pipelines, MLOps, Data Engineering and deploying production-ready scalable systems.

Previous experience in the field of Computer Vision where I’ve been able to publish a co-authored paper during graduation. During my academic experience at the University of Bari I’ve worked on the ISO 25010 certified project e-GLU BOX: a platform for usability tests made for PA. The application was developed both as a web application using Laravel and as a mobile app using Flutter.

Technical skills:

• Python, Pandas, Polars, Numpy, Pyspark, Dash, Prefect
• Delta Lake, Iceberg, DuckDB, Databricks, Azure
• Pytorch, Keras, Tensorflow, Sklearn
• MLFlow, SHAP, Docker
• Flutter, Laravel

Domains of interest:

• Artificial Intelligence, Machine Learning, Data Science, Time Series Forecasting, Computer Vision, Data Engineering

I’m an AI Solutions Engineer at Clockworks in Rotterdam. I studied AI and Data Science in Nijmegen, where I developed a passion for computer vision. Right after graduating, I joined Clockworks, where I’ve been building AI systems for real-world applications for the past four years.

Among other things, I’ve contributed to the Vision AI technology behind the Stack&Track solutions and our own product, Blicker. Through this work, I’ve seen how challenging it is to build systems that perform reliably outside controlled environments.

I’m especially interested in improving data quality, building better annotation workflows, and developing practical Vision AI systems that hold up in production.

Hanna is an AI research engineer at Adyen - a payment processor processing over 40B transactions a year, where she focuses on deep learning research, training infrastructure, and building foundational models. She is passionate about solving large-scale engineering challenges and deploying AI to optimize complex systems and drive real-world impact.

Iryna is a data scientist and co-founder of DataForce Solutions GmbH. At DataForce, the team is building LUML, an open-source, end-to-end AIOps platform that lets teams track experiments, version models, deploy, and monitor—all in one place.

Through his popular AI/ML blog, Jay has helped millions of researchers and engineers visually understand machine learning concepts from Transformers to reasoning LLMs. Jay is the co-author of the bestselling “Hands-On Large Language Models” and the upcoming “An Illustrated Guide to AI Agents” books, published by O’Reilly Media. Jay is Director and Engineering Fellow at Cohere (The Security-first Enterprise AI Company), where he conducts applied research on LLM code generation and code agents.

Data Scientist at ProRail, in the Center of Excellence Advanced Analytics.

Kai Jeggle is a Meteo AI Scientist at Dexter Energy, where he turns high-dimensional weather and satellite data into insights for energy price and power generation forecasting using geospatial AI. He holds a PhD from ETH Zurich, where his research focused on combining machine learning with atmospheric physics, including time at the European Space Agency's Phi-Lab. He previously also worked as a software engineer at Hopsworks, an MLOps startup in Stockholm. Kai has been active in the AI for climate community by leading initiatives at Climate Change AI, a global non-profit working at the intersection of machine learning and climate action.

Konstantinos is a data scientist currently working at Mars with over 3,5 years of experience in the Data Science industry. Notably, is trying to prove and speak loud about model uncalibrated results.

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Senior Data Scientist based in Tallinn, Estonia, with 9+ years of experience in machine learning, analytics, experimentation, and causal inference.

Lin Jia is a Senior Data Scientist at Booking.com, where she works on experimentation, observational causal inference, and generative AI for product and measurement systems at scale. Over the past 9+ years, she has worked across analytics, machine learning, and experimentation, leading initiatives in experimentation methodology, incrementality measurement, and observational causal analysis, as well as LLM-based tooling for experimentation workflows. Her work focuses on bridging rigorous causal methods with practical decision-making in real-world product environments. She is also the creator of Inference & Intelligence Lab, a Podcast and Substack on causal inference, data science, and AI in practice. Her work has been featured at KDD 2024 and the Causal Data Science Meeting 2024.

AI Engineer @xtream. Feel free to reach out!

I work as a Data Scientist at TNO-GDN (the geological survey of the Netherlands). I prototype and develop end-to-end solutions using data science and AI tools to solve challenges related to data management.
I have a scientific background in mechanics of granular materials.
In my free time I love to do sports and contribute to open-source projects.

As a data scientist, Marijn builds data-driven solutions to real-world problems at scale
As a social scientist, he understands how data translates into human behavior and policy impact
As an AI practitioner, Marijn develops and deploys systems that move beyond pilots into real-world use
As a speaker, he shares how data and AI can improve lives globally

Marijn combines technical expertise with a strong foundation in social science to design solutions that don’t just work in theory, but in practice. His work focuses on turning complex data and optimization models into actionable insights for decision-makers.

Over the past years, he has been closely involved in the development of large-scale data platforms such as Enhance, applying multi-objective optimization to help governments design affordable, nutritious, and sustainable food systems.

At Capgemini, Marijn has spent over 9 years leading AI and data science initiatives—from early-stage exploration to production-grade deployments with real-world impact.

He is a frequent speaker on AI, data science, and the gap between experimentation and meaningful adoption.

Most of all, Marijn is driven by one goal: using data to improve people’s lives at scale.

I work in Applied Machine Learning at Adyen, where I focus on deep learning research and training infrastructure. I am passionate about bridging SOTA deep learning with the scale and complexity of the financial industry.

Marysia Winkels is an AI Security Researcher at Gray Swan, and previously worked at Cohere and Xebia Data. She also happily organised, participated, attended and volunteered at previous editions of PyData Amsterdam.

comingHailing from the faraway land of Brentwood, NY and currently residing in the rolling hills of Connecticut, Matt Topol has always been passionate about software. Matt has worked in infrastructure and application development, has lead development teams, and architected large-scale distributed systems for processing analytics on financial data. Matt is a PMC member for the Apache Arrow project, frequently enhancing the Golang library among other enhancements and helping to grow the Arrow Community. He wrote the first book on Apache Arrow "In-Memory Analytics with Apache Arrow" and spent the last couple years working on the Apache Arrow libraries full time and growing the Arrow Golang community. Matt is now a member of the ASF and also a PMC member of Apache Iceberg. Most recently, Matt and two colleagues have started the company, Columnar, focusing on data connectivity using Arrow Database Connectivity (ADBC).

In his spare time, Matt likes to bash his head against a keyboard, develop/run delightfully demented games of fantasy for his victims--er--friends, and share his knowledge with anyone interested who'll listen to his rants.

Machine Learning Engineer with a strong focus on building scalable and robust ML/AI platforms and systems.

Niels van Galen Last is a Staff ML Engineer and Head of AI Engineering, focused on building production-grade AI and ML systems at scale. He leads the architectural foundation of a shared AI platform serving 35+ organizations, operating across distributed and international environments, with a focus on evaluation-driven development, reproducibility, and long-term system reliability.

His work centers on turning experimental models into robust systems: building evaluation frameworks, standardizing LLM and RAG architectures, and designing infrastructure for reliable deployment across cloud and hybrid environments. He has led high-impact AI systems across domains including document AI, optimization, and large-scale ML platforms.

Niels studied Computational and Mathematical Engineering at Stanford University and has held technical leadership roles across startups, consulting, and enterprise environments.

I am a Data Scientist primarily focused on Deep Learning and MLOps. In my spare time I contribute to several open-source python libraries.

Senior Data Scientist at Swap, based in the Netherlands. Background in computational science (MSc, Weizmann Institute of Science) with published research in machine learning for molecular dynamics, materials science, and atmospheric modeling. Previously worked on LLM-based systems, time series forecasting, and large-scale predictive pipelines. Enjoys making graphs and burning electrons.

Pauline is passionate about science and evaluating and optimizing language models for complex tasks. She is Tech Lead Data Science & AI at Sopra Steria and works for government organisations on efficient implementations of LLM and agentic usecases.

Currently a Machine Learning Engineer at Atinary Technologies in Lausanne, Switzerland. Redesigned the training pipeline and introduced an inference infrastructure for the SDLabs platform. Now part of the research team to improve our bayesian optimization and transfer learning algorithms.

Before, I worked as an ML Research Intern at Bose and Logitech on lightweight, causal speech enhancement and source separation under strict latency and memory constraints.

Before that I obtained a MSc in Computer Science from EPFL, with focus on Machine Learning and Digital Signal Processing.

I travel and produce electronic music during my free time!
rayan.daodnathoo.com

Rutger Lit is a Lead Data Scientist at Amsterdam Data Collective (ADC), where he works on pricing, experimentation, and panel data modeling in complex, real-world systems. His work focuses on applying statistical models in Python at scale, with particular attention to high-dimensional data, temporal dependence, and model specification.

Santosh Pingale loves taming large-scale infrastructure problems with open source. His interests span data platforms, distributed systems, performance engineering, and the resilience challenges that appear once systems meet the real world.

Data scientist at Lynxx

Co-founder and CEO of LakeSail and co-creator of Sail. Passionate about building modern, high-performance data systems and making large-scale data processing faster and more accessible for Python users.

Steven is a Software Engineer with 5+ years of experience building and productionizing machine learning systems that solve high-impact business problems. He has worked across domains including user acquisition, customer retention, content generation, supply optimization, and lead generation.

Currently at GetYourGuide, he is part of the Machine Learning and AI Platform team, where he focuses on enabling teams to independently build and operate production-grade LLM systems.

Outside of work, Steven co-organizes the AI in Production meetup in Berlin, a community dedicated to showcasing real-world AI applications across industries. In his free time, he enjoys exploring food and building tools he'd otherwise have to pay for.

Theodore Meynard is a data science manager at GetYourGuide.He leads the evolution of their ranking algorithm, helping customers to find the best activities to book and locations to explore. Beyond work, he is one of the co-organizers of the Pydata Berlin meetup and the conference. When he is not programming, he loves riding his bike and looking for the best bakery-patisserie in town.

Building Python-first data systems at the intersection of modern data stacks and corporate intelligence, with a focus on integration, modelling, and reliability.

Thijs Nieuwdorp is the DevRel Engineer at Polars in Amsterdam. His interest in the interaction between human and computer led him to an education in Artificial Intelligence at the Radboud University, after which he dove straight into the field of Data Science. At Xomnia he witnessed the birth of Polars as Ritchie Vink started working on it during his employment there , and has been using it in his projects ever since. Outside work Thijs enjoys exploring our world through hiking and traveling, and exploring other worlds through books, games, and movies.

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