Product Manager – Data Science

Job not on LinkedIn

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🇪🇺 Europe – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Fundamental

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2024

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Fundamental is an enterprise AI company that builds large tabular models (LTMs) such as NEXUS, pre-trained on billions of tables to detect patterns and predict outcomes from structured data. The company offers an enterprise-grade predictive analytics platform that can be deployed with minimal code or integrated deeply with cloud partners like AWS, emphasizing privacy, security, and scalability. Born from academic research and backed by major investors, Fundamental targets large organizations seeking to extract foresight from their databases and deploy predictive models at cloud scale.

📋 Description

• Own and lead the roadmap and product decisions for 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 AI agents and data science tools across the platform, making common workflows intuitive while preserving flexibility for technical users • Define how users guide agents, inspect their work, and understand results, balancing automation with transparency and control • Own MLOps workflows for managing and monitoring predictive quality of customer models in production • Turn NEXUS capabilities into coherent user experiences 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 • Collaborate with engineering, Applied AI/FDEs, product design, and research teams

🎯 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, demonstrating a solid grasp of core workflows including classification, regression, and time series analysis • Experience 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: uncover the problem behind a request, test assumptions and translate learnings 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 • Data-driven prioritization grounded in usage data, evaluation results, and user evidence • Nice to have: earlier client-facing data science experience followed by product ownership with an engineering team • Nice to have: experience building internal data science tools for Applied AI, solutions teams, or technical customer-facing teams • Nice to have: experience in an early-stage AI company with enterprise customer or forward-deployed exposure

🏖️ 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