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 cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. 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+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, 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 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 ne [... source excerpt omitted ...] ations, 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 q [... source excerpt omitted ...] deep learning - architectures (especially transformers) Ability to translate complex technical concepts into tangible value for both technical and non-technical audiences Strong customer-facing skills, with the ability to independently drive technical conversations and engagements across pre-sales, POCs, and post-sales. Strong problem-solving skills, with the ability to quickly understand unfamiliar customer problems and translate them into practical data science/ML solutions. Broad ML knowledge and the ability to quickly adapt to new domains and problems. Strong communication and collaboration skills. Nice to Have: Master’s or PhD in a quantitative field. Kaggle Grandmas