PyData London 2026

Abby Tse is Chair of PyData NYC, where she has led a community of over 8,000 data professionals since 2022. She organizes the annual PyData NYC/Boston Conference, a three-day event that brings together 600+ attendees from around the world to explore the latest in data science, machine learning, and AI. Abby is currently an MBA student at Columbia Business School, where she focuses on entrepreneurship and innovation. Previously, she worked at IBM, where she built enterprise AI systems, including large-scale generative AI applications to improve knowledge access.

Adam is a Staff Data Scientist at ComplyAdvantage, where they are tackling financial crime with advanced analytics, large-scale systems, and the latest in generative and agentic AI.

Before that, he spent eight years in the smart cities space at HAL24K, helping governments and infrastructure providers make better decisions with their data. Along the way, he built and led a team of ten data scientists and helped launch four spin-out ventures.

A recovering astrophysicist, Adam spent a decade analysing data from space telescopes in search of new cosmic phenomena. He’s since redirected that curiosity toward Earth-based problems.

Adam is an active member of the PyData community, the founder of PyData Southampton, and a long-time volunteer with DataKind UK, supporting charities and NGOs with pro-bono data science.

I am a researcher with a strong track record of transferring core scientific computing skills across very different technical and scientific backgrounds ranging from radiation detection and medical physics to Earth observation. I have worked across disciplines in academic and industry settings and am particularly drawn to complex problems that require continuous learning and close collaboration across different domains.

I study energy policy at the University of Texas at Austin. My work focuses on residential electrification and improving the efficacy of beneficial electrification upgrades.

Arghyadeep Sarkar is a Senior Data Scientist at Red Hat with ~8 years of experience in data science and artificial intelligence. His career has evolved from traditional machine learning to architecting large-scale Generative AI and LLM-based production systems.

He built strong foundations in statistical modeling, ML pipelines, and applied AI, later specializing in deep learning, NLP, transformers, and Generative AI. He has designed and deployed LLM agents, RAG-based systems, and enterprise conversational platforms, covering the full lifecycle from training and fine-tuning to scalable deployment.

Current Focus

• Building reliable agentic AI systems
• Improving retrieval grounding and RAG quality
• Deploying LLMs and SLMs in production
• Delivering scalable, cost-efficient enterprise AI solutions

He brings a system-first engineering mindset, translating cutting-edge AI research into robust real-world products.

A seasoned software engineer, working in both batch and real time, data intensive, python application.

Austen Wallis is a post-graduate researcher at the University of Southampton, specialising in scientific machine learning. His work focuses on developing emulator frameworks that accelerate complex physical simulations by orders of magnitude, utilising high-performance computing and modern AI. While his primary research focuses are in astrophysics, Austen has successfully applied his "fast-forward" techniques across diverse fields, including fusion-energy plasma control at the UK Atomic Energy Authority to extreme weather forecasting at IBM Research. He is a strong advocate for open science and building interactive, reproducible tools using the PyData stack.

Avik is a data scientist, software engineer and a seasoned technical speaker. He loves open-source, writing elegant Python code and views coding as an art.

Ben Vincent is Director of InferenceWorks Ltd and a Principal Data Scientist at PyMC Labs, where he has been building Bayesian solutions for real-world business problems since 2021. He created CausalPy, an open-source Python library for causal inference in quasi-experimental settings. He holds a PhD in Neuroscience from the University of Sussex (UK) and previously held a university faculty position for 15 years.

Carol Chen is a Community Architect at Red Hat, having led several upstream communities including InstructLab, Ansible and ManageIQ. She has been actively involved in open source communities while working for Jolla and Nokia previously. In addition, she also has experiences in software development/integration in her 12 years in the mobile industry. Carol has spoken at events around the world, including AI_Dev in Paris and OpenSearchCon in Shanghai. On a personal note, Carol plays the Timpani in an orchestra in Tampere, Finland, where she now calls home.

Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software developer with a background in Kubernetes, DevOps, and container tools. Focused on open-source software, he both contributes (e.g., Podman, vLLM) and enjoys speaking, with prior experience at Devoxx, WeAreDevelopers, The Linux Foundation, and more. Cedric also spends (too much) time creating video and written content helping developers learn new topics in emerging technologies, with over 2M+ views online. He’s based in New York City and is an organizer of the local Kubernetes Community Day.

After having a career as a Data Scientist and Developer Advocate, Cheuk dedicated her work to the open-source community. Currently, she is working as a developer advocate for JetBrains. She has co-founded Humble Data, a beginner Python workshop that has been happening around the world. Cheuk also started and hosted a Python podcast, PyPodCats, which highlights the achievements of underrepresented members in the community. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.

Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government, including 7 years in pro baseball research with the Philadelphia Phillies, New York Yankees, and Milwaukee Brewers.
He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.

Daniele is a data scientist with expertise in statistics, data science and AI, passionate about exploring the intersection of AI and financial markets.
Since 2023, he is working at MDPI, one of the largest open-access publishers.
A former national 400m sprinter.

Dawn Wages is the Director of Community and Developer Relations at Anaconda, responsible for the most popular AI and ML Python distribution in the world. She is a software engineer, ethical open source advocate, and community leader who previously served as Chair of the Python Software Foundation. Her work emphasizes inclusive practices and sustainable growth in open source ecosystems, combining technical knowledge with attention to equity, sovereignty, and developer collaboration. 

When not working on Python, she enjoys watching Star Trek in Philadelphia with her wife and two dogs.

Dmitry Petrov is the creator of open-source tool DVC (Data Version Control), holds a PhD in Computer Science, previously worked as a Data Scientist at Microsoft, and is now the founder of DataChain.ai, a Python-first data platform for Physical AI.

Feichi Lu is a Data Scientist at MDPI in Basel, where she works on building data-driven analytics for scientific publishing. She holds a Master’s degree in Data Science from ETH Zürich. Her experience spans large-scale data analysis, semantic modeling, and applied AI.

Gabriel Lipnik is an AI engineer and applied mathematician working on production-grade machine learning, artificial intelligence, and optimisation systems. His work focuses on bridging the gap between advanced models and real-world deployment, with a particular interest in MLOps, trustworthy AI, and regulatory-ready ML systems.

He has contributed to large-scale optimization and AI projects in the mobility and infrastructure domain, where reliability, traceability, and operational robustness are critical.

Gabriel is particularly interested in practical approaches to making machine learning systems more transparent, monitorable, and production-ready.

Gergely Daroczi, PhD, has been a passionate open-source package developer for two decades. With over 15 years in the fintech, adtech, healthtech, and other SaaS industries, he has expertise in data science and engineering, as well as cloud infrastructure, in both California and Hungary, with a focus on building scalable data platforms. Gergely maintains a dozen open-source R and Python projects and organizes a tech meetup with 1,800 members in Hungary – along with other open-source and data conferences.

Hitendri Bomble is a Data Scientist at Red Hat, where she builds Generative AI solutions to solve complex business problems. She specializes in working with Large Language Models (LLMs) to create tools that make everyday work more efficient. Deeply rooted in the open-source community, Hitendri focuses on using the latest AI innovations to automate tasks and bring fresh ideas to her team.

Ian is a Scientific Software Developer at QuantStack. He has been an Open Source contributor for over 15 years, is a core maintainer of the visualisation libraries Matplotlib and Bokeh, and lead maintainer of ContourPy. Recently Ian has been involved throughout the Jupyter stack, from kernels and widgets through to JupyterLite.

Ines Montani is a developer specializing in tools for AI and NLP technology. She’s the co-founder and CEO of Explosion and a core developer of spaCy, a popular open-source library for Natural Language Processing in Python, and Prodigy, a modern annotation tool for creating training data for machine learning models.

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source Kubernetes Python framework kr8s in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

I am a senior engineering lead/executive director at Morgan Stanley.

I design and build large-scale, enterprise-ready, high-performance financial systems used in production environments where correctness, resilience, and speed matter. My work spans system design, hands-on engineering, and long-term platform evolution in regulated domains.

I place strong emphasis on clean, maintainable architecture—clear domain boundaries, explicit data contracts, and model-driven design. I optimise for systems that remain understandable and adaptable as complexity, scale, and regulatory demands increase.

A significant part of my work focuses on data analytics, complex data modelling, and financial mathematics—including forecasting, liquidity, risk, and regulatory calculations. I enjoy translating mathematically rich problem spaces and large datasets into precise, explainable, and production-grade implementations.

I work with a prototype-to-production mindset, leveraging modern cloud platforms, data tooling, and AI techniques to move quickly while preserving architectural discipline, observability, and operational robustness.

www.linkedin.com/in/kamlesh-shah

Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA leading the CUDA Education program. For over two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data science, and visualization. Most notably, she has helped lead data initiatives at the University of Texas Austin Applied Research Laboratory, Anaconda, Apple, Expedia Group, Cloudflare, and Snowflake. She is an active volunteer in the Python open-source scientific software community and currently serves on the Advisory Council for NumFOCUS.

The speaker spent over 12 years working in quantitative roles in investment management before returning to academia to study Artificial Intelligence. They are currently completing a Master’s degree in AI and ML in Science, and are particularly interested in how modern machine learning systems behave in practice, especially where modelling assumptions quietly break down.

Ken Obata is a senior data engineer currently working at Lyft, with over seven years of experience building large-scale data infrastructure at KPMG, Amazon, and Lyft. His current research focuses on scalable text deduplication for LLM training data, where he developed a partition-aware MinHash LSH system that processes hundreds of millions of documents on commodity Spark clusters.

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Laura is a very technical designer™️, working at Pydantic as Lead Design Engineer. Her side projects include Sweet Summer Child Score (summerchild.dev) and Ethics Litmus Tests (ethical-litmus.site). Laura is passionate about feminism, digital rights and designing for privacy. She speaks, writes and runs workshops at the intersection of design and technology.

Lena Shakurova is the founder of ParsLabs (https://parslabs.org), a Conversational AI agency, and Chatbotly (https://chatbotly.co), a no-code platform for building AI assistants trained on custom data.

At ParsLabs, she leads a team blending AI, user research and conversation science to design and develop high quality AI Conversations that sound human. She has background in NLP and Artificial intelligence and 8+ years of experience and 110+ successful projects building production-ready chatbots and voice assistants.

Lena focuses on ethical, user-first AI, leveraging her expertise in Linguistics & AI to create responsible, high-quality AI solutions. She shares insights on AI innovation and human-centered design through her blog (https://shakurova.io/blog) and LinkedIn (https://www.linkedin.com/in/lena-shakurova/).

AI Engineer @xtream

Author of Narwhals, heavy contributor to pandas, Polars, and NumPy (stubs). Marco works as Senior Software Engineer at Quansight Labs. His background is in Mathematics. Outside of work he can most likely be spotted at Celtic Folk Sessions.

I am Chief Architect at Engineering is Easy, working in aerospace and defence consulting. I hold a PhD in environmental and geospatial modelling, and I have spent over 20 years across climate research, data science, AI, and developer advocacy.

I also run Living is Easy, where I work as a certified mindset consultant focused on how habits, self-image, and mental programming drive results. That work has given me a deep understanding of how paradigms shape behaviour for both individuals and teams.

Martin O'Reilly is Director of Research Engineering at the Alan Turing Institute, where he leads a team of software, data and infrastructure engineers who work across the Turing's research portfolio to bridge the gap between research and practice - from AI for weather prediction to AI-assisted air-traffic control. Prior to Turing, Martin spent several years developing software, data standards and engineering practices in the education sector before going back to school to build robots and try and understand the brain by modelling it.

Matt Crooks is a Principal Data Scientist at the BBC, where he works in the audiences data science team applying statistical and machine learning models to understand and improve marketing effectiveness and audience engagement. His current work focuses on using data and AI to automate the production of personalised creative assets at scale. Previous work has involved building an ML-powered adaptive learning quiz for BBC Bitesize during Covid. He has also had a previous role leading and developing the experimentation tooling and best practices at Typeform. Matt holds a PhD in Mathematics from the University of Manchester and began his career in academic research into weather and climate.

Ming Zhao is an open source developer and Developer Advocate at IBM Research, where he helps IBM leverage open technologies while building impactful tools and growing vibrant open-source communities. He’s passionate about making open tech accessible to all and ensuring developers have the tools they need to succeed in the rapidly developing AI space. Ming now leads community efforts around Docling, IBM’s fastest-growing open source project, recently welcomed into the LF AI & Data Foundation.

Research Scientist/Engineer at NVIDIA focused on Synthetic Data Generation

I'm a Data-Scientist working in HR Tech and People Analytics with Personio. I'm a big advocate of open source software and regularly contribute to PyMC, PyMC-Marketing and CausalPy. I've worked across a variety of industries ranging from e-commerce, insurance and gambling and in each, i've tried to find ways to apply statistical best practice to business problems.

I'm always open to chat about scientific python, philosophy of science and Bayesian reasoning and decision analysis.

Neal Richardson is VP of Engineering at Posit and a member of the Apache Software Foundation. He is a maintainer of Apache Arrow, along with many other open-source projects. He holds a Ph.D. in Political Science from the University of California, Berkeley.

Nicolas holds a Ph.D. in applied mathematics from Université Paris Dauphine - PSL, where his research focused on machine learning, with particular emphasis on attention mechanisms and geodesic approaches to segmentation. His work on designing advanced deep learning architectures for complex datasets has led to multiple publications at leading international conferences.

He brings hands-on expertise in self-supervised learning and large-scale optimisation, and is currently contributing to Neuralk's mission to develop the first enterprise tabular foundation model.

Niek Tax is a Staff Research Scientist and Tech Lead at Meta's Central Applied Science team in London. He focuses on longer-term, foundational work that addresses new opportunities and challenges across Meta, bridging the gap between academic rigour and product teams. Niek has extensive experience overseeing the end-to-end lifecycle of production-grade ML systems, from research to global deployment. His expertise is in uncertainty quantification, including active learning and probability calibration, and he has published articles at NeurIPS and KDD on those topics.

Before joining Meta, Niek worked as an ML engineer at Booking.com and in applied R&D at Philips Research. He holds a PhD in Computer Science from Eindhoven University of Technology, and has authored 35+ peer-reviewed publications with over 2,500 citations.

PyData London

I am a data scientist at a international mining group.

I am a Senior Machine Learning Scientist at Monzo, where my main focus is around Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and sophisticated data augmentation strategies. With 6 years of experience specializing in Natural Language Processing (NLP), I have a proven track record of building scalable AI systems for high-stakes environments.

Prior to joining Monzo, I was a Machine Learning Engineer at Bumble, leading Trust and Safety initiatives by developing LLM-powered moderation pipelines to ensure platform safety at scale. I also worked as a Senior Data Scientist at ComplyAdvantage, where I applied NLP to financial crime detection, and as a consultant at Sia, focusing on complex question-answering tasks.

I am passionate about the intersection of LLM infrastructure and practical data engineering, specifically solving the "cold-start" problem for niche domains through synthetic data and rigorous validation frameworks

Oreolorun is a machine learning engineer with experience in building AI enabled software features and data processing for AI workflows.

Oriol is a computational statistician, working as a maintainer of the ArviZ and PyMC libraries and as Principal Data Scientist with PyMC Labs. He started in academia but after some years but he left after some years in order to be able to work more freely and collaboratively on open source, software and knowledge sharing. His main areas of interest are data visualization, model and inference diagnostics, model comparison, and prior elicitation. Within open source projects, he has also dedicated a large part of his work to documentation, governance and DEI.

Paddy Mullen is a full‑stack engineer and data‑tooling builder. An early employee at Anaconda, he contributed to the Bokeh visualization library. He has built data tools and led teams at hedge funds and startups. Since 2023 he has been developing Buckaroo, an interactive dataframe viewer for notebook environments. He is now leading visualization at xorq-labs.

Specializes in CPython internals, optimization, and high-performance computing.
Driven by GPU acceleration, CPU vectorization. Evolved from ML systems to CPython core research engineer.
8+ years leading teams in AI, maths, and physics. PyCon speaker.
Lecturer at Moscow Institute of Physics and Technology – top 1 Russian university.
Open to talks and collaboration.

3 wild facts about me:
- In 2025 I hosted ≈70 dogs (not at once). I love mammals — especially 🐶 🐴 People literally pay me to pet-sit — so their pets can live out their best moments in life ✨
- I’ve been to ≈20 countries and lived in 5.5. Korea felt the most exotic/interesting. Indonesia was my favorite.
- In 2024 I went unusually hard on fitness: daily run/swim/bike for 16 weeks. I ran every day for 3 months, logged 75h vigorous + 208h moderate (≈2.5h/day) activities, lost ≈12 kg, got stronger 💪

Prattyush is a Research Software Engineer working in the Granite Feedback Team in IBM Research, based in the UK (Winchester) and the US (New York).

IBM Granite is the family of AI models from IBM and Prattyush leads product and client engagements to increase adoption of the models across various use-cases. He is a technical leader for Agentic and GenAI applications, leading efforts for education content and acts as one of the release managers, contributing to testing and release efforts.

Prattyush is part of the wider AI Foundations organisation and as such regularly contributes to the development of the latest IBM Research technologies, both internally and through open source.

Richard Kehinde Ogunyale is a Senior Software Engineer based in London, UK, with experience building production AI systems, scalable microservices, and machine learning pipelines. He currently works at Partnerize, where he leads projects involving AI-powered solutions, and has previously built RAG systems with vector databases, LLM-powered automation workflows using DAG architectures at scale.

He is passionate about open source, practical AI engineering, and bridging the gap between ML prototypes and reliable production systems.

Samuel Colvin is a Python and Rust developer and Founder of Pydantic Inc., backed by Sequoia to build Pydantic Logfire, the only observability tool that traces your AI and your backend together. The Pydantic library, which he created is downloaded over 580M/month and is a dependency of virtually every GenAI Python libraries including the OpenAI SDK, the Anthropic SDK, the Google Gen AI SDK, Langchain and LlamaIndex.

Sofia is a principal data scientist at Nesta, working with the sustainable future mission team on decarbonising UK homes. During her time at Nesta, Sofia worked with energy performance certificates, social media and smart meter data to: estimate the cost of low carbon heating technologies, identify issues faced by homeowners in their low carbon heating path, understand how people consume energy in their homes and identifying the most suitable low carbon heating technology for groups of homes.

Prior to joining Nesta, Sofia worked as a data scientist at Imperial College London, assessing the accuracy of crowdsourced data for road traffic collision and injury surveillance. Before this she worked as a research fellow at the Social Physics and Complexity research group, LIP Portugal, on health related projects such as identifying antibiotic over-prescription and factors influencing it.

Sofia holds a Bachelor’s degree in Applied Mathematics and Master’s degree in Data Science and Advanced Analytics.

Theo is passionate about NoSQL and distributed computing. He joined Microsoft in 2017 and has been in the Cosmos DB Engineering team as a Program Manager since 2019. He currently focuses on AI, programmability, and developer experience for Azure Cosmos DB. He has a masters degree in Data Science from Dundee University, and lives in the UK with his wife, two boys, and ragcoon cat.

Thomas Ogden is a Senior ML Engineer in Financial Engineering at Spotify. He builds tools, mostly with probabilistic machine learning on sequences and graphs. He once did a PhD in Quantum Optics theory and still thinks about physics a lot.

Tun is a Staff AI Engineer at Lenses, where he leads AI strategy. He is focused on helping companies imagine and implement their strategic vision with agentic AI systems fuelled with real-time context. He was previously a Head of Data and Data Engineer at high growth startups and has spent 20 years building data-intensive applications and leading T-shaped teams.

In his spare time, Tun goes surfing, plays guitar and tends to his analogue cameras.

​Viktor Kessler, is Co-Founder of Vakamo and the creator of Lakekeeper, an Apache Licensed Iceberg REST Catalog. He’s a big believer in open standards like Apache Iceberg, which he sees as the backbone of today’s modern, composable Data & Analytics systems.

A results-driven data professional, focused on hype-free solutions tailored to business needs.

I currently create value at the National Institute of Geophysics and Volcanology, where I develop machine learning models in the Space Weather domain. My work is complemented by finding the hidden stories in data and make them accessible to stakeholders. I studied Physics in Italy (Napoli) and Germany (Frankfurt am Main), previously worked in Analytics within the strategic division of the world's largest professional services network, as well as in the Data Science department of Italy’s leading publishing group.

I am also an organiser of PyData Roma Capitale, actively involved in building the local Python and data science community. Outside of work, I enjoy theatre, discussing finance, and learning new languages.

Hello World, I'm Özge Çinko! 👋

I'm a computer engineer who finds inspiration at the intersection of curiosity and technology. Currently building the future as an AI Engineer at ING.

For me, engineering is a creative craft - turning data into narratives and emotions into visual experiences. I am passionate about making technology more human-centric and purposeful.

When I'm not coding, I'm usually writing, traveling, or chasing the thrill of learning something new.