Applied Scientist, Data Science

WHO WE ARE Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables. We pioneered tabular foundation models: TabPFN v2 was a Nature https://www.nature.com/articles/s41586-024-08328-6 cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics https://www.oxcan.org/news/prior-labs-and-oxford-cancer-analytics-partner-to-advance-liquid-biopsy-and-clinical-decision-making-in-lung-disease to preventing train failures with Hitachi https://siliconangle.com/2025/12/01/prior-labs-debuts-tabular-ai-foundation-model-scales-10-million-rows/. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level. We're a small, highly selective team of 40+ https://priorlabs.ai/about with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter https://www.linkedin.com/in/frank-hutter-9190b24b/, Noah Hollmann https://www.linkedin.com/in/noah-hollmann-668b9010b/, and Sauraj Gambhir https://www.linkedin.com/in/sauraj-g/, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun. In July 2026, less than 18 months after our €9M pre-seed, we joined SAP https://priorlabs.ai/blog-posts/priorlabs-sap as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years. ABOUT THE ROLE You'll join our data science team working with an entirely new class of AI models. As a Data Scientist at Prior Labs, you'll be the critical link between our foundation models and real-world applications — experimenting hands-on with our tabular foundation models (including TabPFN) to uncover new applications, working directly with customers to show how native tabular AI solves problems traditional methods can't, and translating what you learn back into our product roadmap. HOW YOU'LL DRIVE IMPACT: Applied Data Science & Experimentation: Identify high-impact use cases for TFMs and build proof-of-concepts that showcase their advantages over traditional ML. Develop best-practice workflows using capabilities like in-context learning (ICL) and benchmark rigorously against existing approaches. Customer Success: Work directly with users to understand their challenges and demonstrate TFM value through technical demos tied to real business objectives. Guide onboarding to deliver quick wins and translate user feedback into technical insights for our product team. Community & Education: Design and deliver workshops, tutorials, and content that explains the tabular foundation model paradigm — how it differs from LLMs and traditional ML, and why it matters. Engage the data science community through Kaggle, GitHub, and public-facing work. WHAT WE'RE LOOKING FOR: - PhD or Master's in a quantitative field, plus 3+ years of experience building and deploying ML/AI in industry, competitive ML, or open-source. - Deep proficiency in Python and the data science ecosystem, with hands-on experience training and deploying deep learning models in PyTorch, including modern deep learning - architectures (especially transformers) - Collaborative development on GitHub and strong software engineering practices - Ability to translate complex technical concepts into tangible value for both technical and non-technical audiences - Genuine curiosity about new model architectures and a drive to explore what they can do NICE TO HAVE: - Kaggle Grandmaster, Master, or Expert status - Experience in technical consulting, solutions engineering, MLOps, or developer advocacy - Contributions to open-source libraries or data science tooling - A portfolio of blog posts, talks, or projects that demonstrate strong technical communication LIFE AT PRIOR LABS You'll work alongside researchers a…