Data Scientist – Extensions

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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

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types • Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability • Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably • Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization • Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting • Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work • Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities • Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently

🎯 Requirements

• 5+ years of experience in data science or machine learning roles • Strong Python skills, including fluency with pandas, numpy, and scikit-learn • Deep hands-on experience with traditional ML models: XGBoost, LightGBM, CatBoost, and similar gradient boosting frameworks • Solid understanding of what makes real-world tabular data challenging: class imbalance, high cardinality, distribution shift, missing values, and more • Strong experimental mindset - comfortable designing benchmarks and drawing rigorous conclusions from noisy results • Ability to work autonomously and drive work from idea to shipped output

🏖️ 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 mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action