Research Scientist – Analytic Learning Algorithms

About the role

This fully remote research position focuses on the design and analysis of learning algorithms built on modular probabilistic structures. You will work at the intersection of machine‑learning theory, probabilistic modeling, and applied research, contributing to high‑impact projects in finance, scientific discovery, and quantitative analysis.

Key responsibilities

• Develop numerical and analytical models for learning systems based on modular probabilistic architectures.
• Design and analyze learning algorithms, ensuring theoretical soundness and practical applicability.
• Prove algorithmic properties and validate findings through rigorous experimental evaluation.
• Translate concepts from academic literature into implementable models and system components.
• Write clean, well‑documented code supporting research experiments and production‑aligned implementations.
• Collaborate closely with other researchers to integrate insights across different areas of expertise.
• Help advance system design by ensuring coherence between mathematical reasoning and software implementation.

Required profile

• Advanced degree (PhD preferred) in Mathematics, Computer Science, Statistics, or a related quantitative field.
• Strong background in mathematical analysis methods such as optimal transport, information geometry, or continuous optimization.
• Experience with probabilistic graphical models, including factor graphs.
• Familiarity with tractable density‑estimation techniques such as normalizing flows, autoregressive models, or probabilistic circuits.
• Ability to bridge theoretical reasoning and practical implementation in code.
• Excellent communication skills and a research‑oriented mindset.

Required skills

• Probabilistic graphical models
• Factor graphs
• Normalizing flows
• Autoregressive models
• Probabilistic circuits
• Optimal transport
• Information geometry
• Continuous optimization

What we offer

• Fully remote work within a globally distributed, research‑focused team (CET‑aligned).
• Opportunity to tackle cutting‑edge theoretical problems with direct real‑world applications.
• High level of autonomy in a flat, research‑driven environment.
• Exposure to interdisciplinary applications across finance, physics, and other high‑stakes domains.