Data Scientist - Extensions
At a glance
Mid-level Data Science role at Fundamental. Remote, Israel · full-time.
Pay not stated Data Scientist salary
remoteroles summary, based on the employer's posting.
What you'll do
- Develop methods that raise NEXUS prediction quality across enterprise datasets, industries and task types
- Build reliable, reusable Python components, with correctness and production use in mind
- Study real-world tabular data challenges and devise ways for NEXUS to handle them consistently
- Test new approaches with rigorous experiments and benchmarks, then use the evidence to set priorities
- Validate methods on customer data with Applied AI Engineers and turn findings into product capabilities
What you bring
- At least five years in data science or machine learning roles
- Strong Python skills, including pandas, numpy and scikit-learn
- Practical experience with gradient-boosted models such as XGBoost, LightGBM and CatBoost
- A solid grasp of tabular data issues, including class imbalance, high cardinality, distribution shifts and missing values
- You can design sound experiments and move work independently from an initial idea through delivery
Who this fits
This remote role suits a mid-level data scientist who can take ownership of work and collaborate with research, engineering and applied AI teams. You'll work on structured prediction problems and deliver production-ready capabilities.
From the employer
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
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