About Fundamental
Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by 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
5+ years in data science/ML, strong Python with pandas/numpy/scikit-learn, experience with XGBoost/LightGBM/CatBoost, experimental mindset, autonomous, production-quality implementation skills.
Key Responsibilities
- researching methods
- implementing components
- validating approaches
Skills & Tools
Python, pandas, numpy, scikit-learn, XGBoost, LightGBM, CatBoost, TabPFN, CARTE, DuckDB, Polars, NEXUS
Job Details
- Category: Data and Analytics
- Seniority: Senior Level
- Commitment: Full Time
- Workplace: Remote — Europe or Tel Aviv or United States
- Languages: English
Benefits
- Generous parental leave
- Relocation assistance
About Fundamental
An AI company building large tabular models to improve enterprise decision-making. — Industry: Information Technology