Research Data Scientist

Responsibilities

• Identify, characterize, and evaluate high-value data sources including real-world, synthetic, and SCM-generated data
• Design synthetic data generation approaches based on Structural Causal Models and probabilistic models
• Build tools and workflows to generate, validate, and benchmark synthetic datasets at scale
• Develop metrics and evaluation procedures for synthetic data quality
• Collaborate with research and engineering teams to maintain reliable training pipelines

Requirements

• Experience with synthetic data generation for structured or tabular data
• Expertise in Structural Causal Models, causal inference, or simulation-based data generation
• Strong fundamentals in statistics, probability, and applied machine learning
• Proficiency in Python and the scientific computing stack (numpy, pandas, scipy, scikit-learn)
• Experience designing quantitative analyses to assess data quality, realism, and bias

Preferred Qualifications

• PhD or MSc in a quantitative field such as Computer Science, Statistics, or Physics
• Contributions to open source ML or causal inference projects
• Experience with probabilistic programming frameworks or simulation environments
• Experience working with enterprise tabular data and predictive analytics

About the Company

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 drive enterprise decisions.