Research Scientist – Analytic Learning Algorithms

About the role

We are seeking a Research Scientist to advance analytic learning algorithms at the intersection of machine‑learning theory, probabilistic modeling, and applied research. The role is fully remote and collaborates with a distributed, CET‑aligned team, focusing on building modular probabilistic structures for high‑impact domains such as finance and scientific discovery.

Key responsibilities

• Develop numerical and analytical models based on modular probabilistic architectures.
• Design, analyze, and prove properties of learning algorithms ensuring theoretical soundness and practical applicability.
• Translate concepts from academic literature into implementable system components.
• Produce clean, well‑documented code for research experiments and production‑aligned implementations.
• Collaborate with fellow researchers to integrate insights across multiple areas of expertise.

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 and tractable density‑estimation techniques.
• Research‑oriented mindset with rigorous analytical thinking.
• Excellent communication skills for effective collaboration in a distributed environment.

Required skills

• Probabilistic graphical models (e.g., 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.
• Opportunity to tackle cutting‑edge theoretical problems with real‑world impact.
• High autonomy in a flat, research‑driven environment.
• Exposure to interdisciplinary applications across finance, science, and quantitative analysis.