Data Scientist - Extensions

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

In this role, you'll research, develop, and productize data science capabilities that enhance and expand our product performance on real enterprise use cases - working across a wide range of prediction tasks, data types, and business domains. You'll go deep on hard data science problems, collaborate closely with R&D on product capabilities, and ship production-grade work that has a direct impact on Production use cases.

Key responsibilities

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

Must have

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

Nice to have

Familiarity with tabular foundation models (TabPFN, CARTE, or similar)

Competitive data science experience (Kaggle, DrivenData, or similar) - especially top finishes on tabular competitions

Background in a domain where structured prediction matters: finance, supply chain, healthcare, retail, or industrial

Experience contributing to or designing internal ML libraries or shared tooling

Familiarity with DuckDB, Polars, or modern in-process analytics engines

Comfort reading ML research papers and translating findings into practical implementations

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