About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI. About the role This is a key role as we build out our Product team from the ground up. You’ll be building a novel experience for data science practitioners when building predictions with NEXUS at the core. Owning the product that enables them to run their workflows end to end with NEXUS. You’ll build AI agents and data science tools used by data scientists in a self-serve workflow. Working closely with engineering, Applied AI (FDEs), Product design teams and Research, you’ll contribute to shape the product layer on top of NEXUS. You'll learn from customer discovery and turn those insights into a roadmap that makes data scientists more effective. What you’ll do Own and lead the roadmap and product decisions for our data science products, from customer discovery through product definition, launch and iteration. Work directly with data scientists to understand their needs and usage patterns Shape how our AI agents and data science tools work across our platform layer, making common workflows intuitive while preserving the flexibility technical users need. Define how users guide agents, inspect their work and understand results, balancing automation with transparency and control. MLOps: own the workflows customers use to manage and monitor predictive quality of their models in production Turn NEXUS capabilities into coherent experiences for your users, with clear product requirements and practical tradeoffs. Define success measures for usability, adoption and customer outcomes; use data, feedback and product evidence to guide priorities. Must have experience 4+ years of experience owning and shipping ML or data science products with an engineering team, from discovery and roadmap decisions through delivery and iteration. 2+ years hands-on expertise in data science, demonstrating a solid grasp of core workflows including classification, regression, and time series analysis. Working with structured or tabular data and predictive ML workflows. Experience building agentic products, with practical understanding of their limitations and the interaction patterns that help users trust and control them. Strong customer discovery skills: you can uncover the problem behind a request, test assumptions and translate what you learn into product decisions. Technical fluency and strong collaboration with engineers and applied data scientists, with the ability to turn ambiguity into a clear, deliverable scope. Are data-driven: you ground prioritization in usage data, eval results, and evidence from users rather than intuition alone Nice to have experience Earlier experience in client-facing data science, followed by ownership of a product developed with an engineering team. Building internal data science tools for Applied AI, solutions teams or other technical customer-facing teams. Working in an early-stage AI company, with direct exposure to enterprise customers or a forward-deployed motion.

Benefits

Competitive compensation with salary and equity Comprehensive health coverage for you and your dependents Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys Relocation support for employees moving to join the team in one of our office locations A high-trust, low-ego culture that values diversity of thought, ownership and initiative, with respect for personal boundaries and working styles