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Fundamental is an AI research lab pioneering enterprise decision-making with NEXUS, the world's most powerful Large Tabular Model (LTM) purpose-built for structured business data. Backed by $275M in funding and trusted by Fortune 100 companies, Fundamental is transforming how enterprises leverage AI for predictions.
You will own the product experience for data science practitioners building predictions with NEXUS at the core. This role combines product strategy, customer discovery, and technical leadership to build an end-to-end workflow platform that makes data scientists more effective.
Key responsibilities include:
- Owning and leading the roadmap and product decisions for data science products, from customer discovery through definition, launch, and iteration
- Working directly with data scientists to understand their needs, usage patterns, and pain points
- Shaping how AI agents and data science tools work across the platform layer, balancing intuitive workflows with technical flexibility
- Defining how users guide agents, inspect their work, and understand results while maintaining transparency and control
- Owning MLOps workflows that enable customers to manage and monitor predictive model quality in production
- Translating NEXUS capabilities into coherent user experiences with clear product requirements and practical tradeoffs
- Defining success metrics for usability, adoption, and customer outcomes; using data and evidence to guide priorities
You'll collaborate closely with engineering, Applied AI teams, product design, and research to shape the product layer on top of NEXUS.
REQUIREMENTS:
- 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 with solid grasp of core workflows including classification, regression, and time series analysis
- Working experience with structured or tabular data and predictive ML workflows
- Experience building agentic products with practical understanding of their limitations and interaction patterns that help users trust and control them
- Strong customer discovery skills: ability to uncover problems behind requests, test assumptions, and translate learnings into product decisions
- Technical fluency and strong collaboration with engineers and applied data scientists; ability to turn ambiguity into clear, deliverable scope
- Data-driven approach: grounding prioritization in usage data, eval results, and user evidence rather than intuition
NICE TO HAVE:
- Earlier experience in client-facing data science followed by product ownership with an engineering team
- Building internal data science tools for Applied AI, solutions teams, or other technical customer-facing teams
- Experience at early-stage AI companies with direct exposure to enterprise customers or forward-deployed motion
