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$ cat jobs/principal-technical-product-manager-data-platform-sandboxaq-ec78c4fde580.json

Principal Technical Product Manager, Data Platform

Sandboxaq·Canada·senior
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ABOUT SANDBOXAQ SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors. We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders. At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact. THE OPPORTUNITY SandboxAQ’s AI Simulation (AISim) division develops and applies Large Quantitative Models (LQMs) that are grounded in physics, chemistry, mathematics and biology to simulate molecular behavior with scientific precision and accelerate discovery in the physical world across biopharma, chemicals, advanced materials, healthcare, and so much more. The AISim division is looking for a seasoned Product Manager to join this team to build and own the Data Platform that underpins AISim’s scientific discovery platform spanning Drug Discovery (BioSim) and Materials Design (ChemSim) applications. This includes the end-to-end data management lifecycle including how data is ingested from CROs and lab partners, automatically detected and reconciled, tracked end-to-end for lineage and provenance, and governed by sensitivity and field-of-use controls. Your charter spans the full journey of a dataset; from the moment it lands to the moment it is safely used in a model or expor