About FundamentalFundamental is an AI company pioneering the future of enterprise decision-making. Founded by Deep Mind 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.Key responsibilitiesAs part of the Research team, you will contribute to the development of breakthrough machine learning models by working on one of the most important frontiers in model training and evaluation: high-quality real and synthetic data.This role is especially focused on synthetic data generation, Structural Causal Models (SCMs), and realistic simulation-based data sources. You will help us design, evaluate, and scale datasets that capture the structure, dependencies, and edge cases needed to train foundation models for enterprise tabular data.The main responsibilities of this role are:Identifying, characterizing, and evaluating high-value data sources for training and evaluating ML models, including real-world data, synthetic data, SCM-generated data, and physical or systems-based simulator outputsDesigning and analysing synthetic data generation approaches based on Structural Causal Models, probabilistic models, simulators,



and other mechanisms that capture realistic relationships between variablesWorking with researchers to define what makes a synthetic dataset useful, realistic, diverse, causally meaningful, and appropriate for model training or evaluationBuilding tools and workflows to generate, validate, benchmark, and iterate on synthetic datasets at scaleDeveloping metrics and evaluation procedures for synthetic data qualityTransforming structured, unstructured, simulated, and causally generated data into formats suitable for training and evaluating large-scale ML modelsCollaborating with the research team to maintain a reliable, efficient training pipeline where data quality, data diversity, and synthetic data generation are critical componentsCollaborating with the wider engineering and infrastructure team to ensure data generation and processing workflows are scalable, reproducible, and robustMust haveExperience with:Synthetic data generation for machine learning, especially for structured or tabular dataStructural Causal Models, causal graphs, causal inference, probabilistic modelling, or simulation-based data generationIdentifying and evaluating high-quality data sources to train and evaluate ML models, including both real-world and realistic synthetic data sourcesBringing data from structured and unstructured sources, simulators, causal models, or generative processes into formats accessible by ML modelsDesigning quantitative analyses to assess data quality, realism, diversity, bias,



coverage, and downstream model performanceStrong fundamentals in:Statistics, probability, and applied machine learningData science workflows, including exploratory analysis, feature understanding, validation, and experimental designSoftware engineering for research-grade and production-grade data workflowsStrong knowledge of:Python data processing and scientific computing stack, including numpy, pandas, scipy, scikit-learn, or similar toolsFamiliarity with:Causal modelling, graphical models, probabilistic programming, agent-based simulation, discrete-event simulation, or physical / systems-based simulatorsData storage and data versioning solutionsClassical machine learning and deep learning methods, especially outside of purely LLM-based workflowsNice to haveContributions to open source ML, causal inference, synthetic data, simulation, or data science projectsBSc, MSc, or Ph D in computer science, machine learning, statistics, mathematics, physics, engineering, economics, or another quantitative fieldExperience working with tabular data, predictive analytics, or enterprise decision-making systemsExperience building or evaluating synthetic datasets for model trainingExperience with SCM libraries, probabilistic programming frameworks, simulation environments, or custom data generation pipelinesBenefitsCompetitive compensation with salary and equityComprehensive health coverage for you and your dependentsPaid parental leave for all new parents, inclusive of adoptive and surrogate journeysRelocation support for employees moving to join the team in one of our office locationsA mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

📌 Research Data Scientist (Barcelona)
🏢 Fundamental
📍 Barcelona